Modifier and catalyst performance optimization for ethylene epoxidation.

Abstract

A method for maximizing the selectivity (S) of an epoxidation catalyst in an ethylene oxide reactor system, comprising: measuring the reactor selectivity (S) measured from an ethylene oxide production system configured to convert a feed gas comprising ethylene and oxygen to ethylene oxide in the presence of an epoxidation catalyst and a chloride-containing catalyst modifier in the ethylene oxide reactor system. meas ), the measured reactor temperature (T meas ), and one or more operational parameters. The epoxidation catalyst comprises silver and a promoting amount of rhenium (Re), and the measured reactor selectivity (S meas ), the measured reactor temperature (T meas ), and the one or more operational parameters include real-time and historical operational data points over time generated by the ethylene oxide production system. The method also includes (a) using the model to determine, for each time point, an optimal regulator level (M opt ) and a model-estimated temperature (Test) of the epoxidation catalyst. est ) and a model estimated temperature (Test) are determined based on at least one operational parameter of the one or more operational parameters at the time point, the at least one operational parameter not including a moderator level, and the model is based at least in part on empirical historical data associated with the epoxidation catalyst, the ethylene oxide production system, or both. The method also includes, using a processor, (b) calculating, for each of the time points, the measured reactor selectivity (S meas ) and model estimation selectivity (S est ) and the measured reactor temperature (T meas (c) curve fitting the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve; (d) determining the difference (ΔT) between the delta selectivity (ΔS) data points and the model estimated temperature (Test); (e) determining the difference (ΔT) between the delta selectivity (ΔS) data points and the model estimated temperature (Test); (f) determining the difference (ΔT) between the delta selectivity (ΔS) data points and the model estimated temperature (Test); (g) determining the difference (ΔT) between the delta selectivity (ΔS) data points and the model estimated temperature (Test); (h) determining the difference (ΔT) between the delta selectivity (ΔS) data points and the model estimated temperature (Test); (i) determining the difference (ΔT) between the delta selectivity (ΔS) data points and the model estimated temperature (Test); (j ... real-time ) and ΔT(ΔT real-time Based on the real-time value of real-time) and (e) real-time RCl eff using the processor to output an actionable recommendation based on the input signal. The method further includes using the processor to (f) display the actionable recommendation on a display.

Description

[Technical field]

[0001] The present invention relates generally to determining maximum catalyst selectivity for ethylene epoxidation. More specifically, the present invention relates to a system and method for determining optimal moderator levels to achieve maximum catalyst selectivity in real time. [Background technology]

[0002] Ethylene oxide (EO) is a valuable chemical product well known for its use as a versatile chemical intermediate in the production of a wide variety of chemicals and products. For example, EO is often used to produce ethylene glycol, which is used in many different applications and can be found in a wide variety of products including automobile engine antifreeze, hydraulic brake fluid, resins, fibers, solvents, paints, plastics, films, household and industrial cleaning products, pharmaceutical preparations, and personal care items such as cosmetics and shampoos, among others.

[0003] In the commercial production of EO, ethylene (C 2 H 4 ) is converted to oxygen (O 2 ) in the reaction. Catalyst performance can be evaluated based on selectivity, activity, and operational stability. The selectivity (S) of an ethylene epoxidation catalyst, also known as "efficiency," is the ratio of the efficiency with which an ethylene epoxidation catalyst converts ethylene into competing by-products, i.e., carbon dioxide (CO 2 ) and water (H 2 O) to the desired reaction product (i.e., EO).

[0004] Activity refers to the rate of the epoxidation reaction and is usually described as the temperature (T) required to maintain a given rate of EO production by the epoxidation catalyst. The stability of an ethylene epoxidation catalyst refers to how the selectivity and / or activity of the process changes while a catalyst charge is in use, i.e., more EO is produced over time.

[0005] There are various approaches to improve the performance of ethylene epoxidation catalysts, including improvements in selectivity, activity and stability. For example, certain silver-based ethylene epoxidation catalysts, often referred to as "high selectivity" catalysts, contain a rhenium (Re) promoter in addition to silver, as disclosed, for example, in U.S. Pat. Nos. 4,761,394(A) and 4,766,105(A). Optionally, certain silver-based ethylene epoxidation catalysts may also contain one or more additional promoters, such as alkali metals (e.g., cesium and lithium), alkaline earth metals (e.g., magnesium), transition metals (e.g., tungsten) and main group nonmetals (e.g., sulfur). In addition to improved catalyst formulations, catalyst modifiers, also commonly referred to as reaction modifiers, have been found that can be added to the reactor feed gas to improve selectivity. Such modifiers reduce the CO2 production of ethylene or EO compared to the desired formation of EO. 2 and inhibiting undesirable oxidation to water.

[0006] Suitable catalyst modifiers for the highly selective silver epoxidation catalysts are, for example, organic halides such as methyl chloride, ethyl chloride, ethylene dichloride or vinyl chloride.

[0007] However, although the addition of catalyst modifiers generally improves the performance of high selectivity silver epoxidation catalysts, i.e., catalysts having silver (Ag), rhenium (Re) and one or more alkali metal promoters on a solid refractory support, these catalysts nevertheless deteriorate over time and their activity decreases. Thus, as the catalyst deteriorates, the epoxidation reaction temperature is increased over time to maintain ethylene oxide production at the desired level.

[0008] Additionally, when using many highly selective silver epoxidation catalysts, the EO production parameters, gas hourly space velocity (GHSV), reactor inlet pressure, and O2, as discussed, for example, in EP 0 352 850 A1, U.S. Pat. Nos. 7,193,094 B2, 8,362,284 B2, WO 2010 / 123842 A1, U.S. Pat. Nos. 9,221,776 B2, and 10,208,005 B2, are important factors influencing the selection of the catalyst. 2 , C 2 H 4 , CO 2 , and H 2 As operating conditions, such as the reactor feed concentration of O, change, the moderator concentration in the reactor feed gas (e.g., the feed gas entering the EO reactor) must be adjusted to maintain maximum catalyst selectivity.

[0009] When applying a moderator, it is generally accepted that the moderator concentration in the reactor feed gas should be selected so that catalyst selectivity is maintained at a maximum. The chemistry underlying the determination of the optimal moderator level depends on the surface concentration of chlorides rather than the gas phase concentration. The surface concentration of chlorides is the result of adsorption and desorption phenomena, which in turn depends on many factors. Important factors include the gas phase concentration of the moderator species, the catalyst surface concentration of the catalyst dopant, the gas phase concentration of hydrocarbons that can scavenge chlorides, the reaction temperature, the concentrations of other species that affect the catalyst surface coverage, and the kinetics of chloride adsorption / desorption, which can take several hours or more.

[0010] EO catalyst operators use a variety of methods to introduce and control the level of chloride on the catalyst. The moderator level (M) can be controlled by measuring and varying the chloride feed concentration or by the fresh feed rate of moderator to the reactor. Other structures have been used to control the moderator level to normalize the chloride level with respect to the hydrocarbons that can remove the chloride from the catalyst.

[0011] As disclosed in WO 03 / 044002 A1 and WO 2005 / 035513 A1, another way to define and control the moderator level is to consider the effect of hydrocarbon concentration on the surface chloride level by using an effective chloride level that applies the ratio of the weighted sum of gas phase chloride concentrations to the weighted sum of hydrocarbon concentrations. These approaches capture the steady state effect of changes in gas phase chloride and hydrocarbon concentrations on the equilibrium chloride concentration on the catalyst surface. However, other factors such as changes in temperature and operating conditions can also affect the surface chloride concentration and the optimal moderator level.

[0012] Existing techniques for optimizing the moderator level (M) include cyclically varying (i.e., stepwise) the moderator level (M) and observing the selectivity and activity response of the catalyst. The point of maximum selectivity (S opt ) is typically selected after this stepwise optimization. opt The regulator level (M) at which the point is obtained is the "optimal" regulator level (M opt ), the process is repeated periodically or when significant changes in operating conditions occur. However, stepping the modifier level (M) is a manual process performed by the operator of the EO generation system, which can be tedious and inefficient.

[0013] Furthermore, it can be difficult to gauge whether a change in moderator level (M) is sufficient to observe an improvement in catalyst selectivity outside the overall noise of the process complicated by changes in operating conditions.

[0014] The delay between changes in moderator level (M) and complete equilibration of the catalyst surface and the impact on catalyst performance can also present a challenge. Additionally, accurate measurement of moderator concentration or normalized gas phase chloride concentration in the feed gas can be difficult, especially in an industrial plant environment, making optimization of moderator levels unreliable.

[0015] Certain existing techniques for optimizing moderator levels include monitoring the ratio of the weighted sum of the chloride concentration to the weighted sum of the vapor-phase moderator concentration or hydrocarbon concentrations in the reactor feed gas, and relating the optimum level to temperature. For example, U.S. Pat. No. 7,193,094 (B2) discloses a process that relies on changes in temperature and the ratio of the effective molar amount of active moderator species (i.e., chloride) in the feed gas to the effective molar amount of hydrocarbons present in the feed gas. Similarly, U.S. Pat. No. 9,221,776 (B2) discloses a process that relies on changes in temperature and the ratio of the effective molar amount of active moderator species (i.e., chloride) in the feed gas to the effective molar amount of hydrocarbons present in the feed gas to maximize catalyst selectivity (S opt In order to maintain the maximum catalyst selectivity (S), the change in temperature is correlated with the change in the concentration of the moderator via an exponential relationship. For example, in U.S. Pat. No. 9,221,776 (B2), opt The regulator level is adjusted whenever the temperature changes, without considering other factors that may affect the

[0016] Optimal Regulator Level (M opt These methods of selecting the chloride concentration have limitations that reduce their applicability in industrial EO units. For example, one limitation is that the method requires accurate and precise measurement of gas phase chloride, which can be difficult to achieve in an industrial plant environment. Also, gas phase chloride concentration, or normalized forms as defined in WO 03 / 044002 A1, U.S. Pat. No. 7,193,094 B2, or WO 2005 / 035513 A1, do not always indicate the surface chloride level that determines catalyst performance. The adsorption and desorption kinetics can take hours to days, meaning that there is a delay in the effect on catalyst performance. The adsorption and desorption kinetics complicate optimization in an industrial plant environment.

[0017] Finally, even at steady state temperature and hydrocarbon concentration, there are other factors that affect the optimal chloride level. Non-limiting examples include non-hydrocarbon species concentrations (e.g., CO 2 ) and catalyst age, in addition to their effects on temperature, can affect the optimum chloride level on the catalyst surface.

[0018] U.S. Pat. No. 9,174,928 (B2) describes a process for the epoxidation of ethylene, comprising: (a) after start-up, contacting an epoxidation catalyst comprising a silver and rhenium promoter with a feed composition comprising a first concentration of ethylene, a first concentration of oxygen, a first concentration of carbon dioxide that is less than 2.0 volume percent, and a first concentration of a chloride modifier to produce a desired work rate W at a first operating temperature; 1 To achieve the above, (b) following step (a), increasing a desired operating rate W to increase the first operating temperature to a second operating temperature. 1 adjusting the feed composition while maintaining (i) reducing a first concentration of ethylene to a second concentration of ethylene; (ii) reducing the first concentration of oxygen to a second concentration of oxygen; (iii) increasing the first concentration of carbon dioxide to a second concentration of carbon dioxide; and (iv) decreasing or increasing a first concentration of the chloride adjuster to a second concentration of the chloride adjuster; (c) Following step (b), the desired work speed W 1 at the second operating temperature, wherein further adjusting the feed composition includes: (i) increasing the second concentration of ethylene to a third concentration of ethylene; (ii) increasing the second concentration of oxygen to a third concentration of oxygen; (iii) reducing the second concentration of carbon dioxide to a third concentration of carbon dioxide; and (iv) adjusting, including one or more of: increasing or decreasing the second concentration of the chloride ion adjuster to a third concentration of the chloride ion adjuster.

[0019] The method described in U.S. Patent No. 9,174,928 B2 provides guidance on modifying conditions to obtain the temperature that maximizes selectivity for a given work rate by varying conditions such as ethylene concentration or oxygen concentration. Moderator levels also need to be altered to maintain optimal selectivity, but no specific guidance as to appropriate optimum levels is given by the method.

[0020] U.S. Patent No. 8,362,284 (B2) describes a chlorination effectiveness parameter (Z) that is related to temperature or concentration of a moderator to achieve a desired EO production level or some other desired goal. * However, the art does not establish a method for determining the initial optimum moderator level in a catalyst operation.

[0021] Rather, this approach first assumes optimal operation and then simply evaluates whether the chlorides remain close to the optimum after the conditions are changed, and provides guidance as to the changes necessary to re-achieve the optimal chloride levels. Furthermore, this technique allows the operator to vary two parameters at a time (i.e., temperature or Z) while keeping other process conditions (e.g., GHSV, pressure, feed gas composition, etc.) substantially fixed. * ) needs to be varied. In typical industrial EO production, these other conditions often change over time due to deliberate changes or process disturbances. Therefore, this technique is not robust to disturbances that occur during typical EO production plant operations.

[0022] WO 2016 / 108975(A1) estimates the direction of the chloride optimum versus a given optimum when operating conditions such as EO production rate change. Like other techniques, WO 2016 / 108975(A1) requires knowledge of the optimum during catalyst operation prior to changing operating conditions. Furthermore, this technique is limited to the use of data collected over a short period of time (e.g., 7 days). While the information obtained from this technique is directional (e.g., indicating whether the catalyst is over- or under-regulated), it does not provide the magnitude by which the regulator level should be adjusted to achieve maximum catalyst selectivity. Thus, the operator of the EO production system is left to adjust the regulator level on an ad-hoc basis in a specified direction to find the regulator level that achieves maximum catalyst selectivity, which is not robust.

[0023] No. 9,892,238 (B2) describes a system for monitoring a process determined by a set of process data in a multi-dimensional process data domain relating to process input-output data, the system comprising: means for acquiring a plurality of historical process data sets; means for obtaining a transformation from the multi-dimensional process data domain to a lower dimensional model data domain by performing multivariate data analysis; means for transforming the current process data set into a model data set for monitoring the process using the obtained transformation; and means for detecting persistent changes in a process characteristic of the process that are no longer captured by multivariate data analysis based on observing residuals that exceed a predetermined threshold for a predetermined amount of time.

[0024] The system and method of US9892238B2 applies a combination of artificial neural nets and principal component analysis to distinguish normal operating modes from abnormal or previously seen operating modes. One example is the detection of catalyst over-regulation in a process for ethylene epoxidation. However, once an over-regulation condition is detected, US9892238B2 does not provide specific steps for the plant operator to return to optimal conditions. The plant operator must still apply manual intervention to determine and return the system to optimal operating conditions.

[0025] The maximum selectivity (S) of the silver-based ethylene epoxidation catalyst at the current set of operating conditions was determined due to the effect of the moderator level on the selectivity of the highly selective epoxidation catalyst. opt Optimal regulator levels (M opt It may be desirable to have a regulator level optimization technique that accurately and robustly determines the optimal regulator level (M opt Instead of changing the modifier level stepwise until a maximum catalyst selectivity (S) is found, the modifier level is adjusted automatically or by an operator by a given amount to find the maximum catalyst selectivity (S opt ) can be achieved.

[0026] As described in further detail below, the present disclosure overcomes the limitations of existing technologies and provides a robust and effective technique for optimizing regulator levels. [Prior art documents] [Patent documents]

[0027] [Patent Document 1] U.S. Pat. No. 4,761,394 [Patent Document 2] U.S. Pat. No. 4,766,105 [Patent Document 3] European Patent No. 0352850 [Patent Document 4] U.S. Patent No. 7,193,094 [Patent Document 5] U.S. Patent No. 8,362,284 [Patent Document 6] International Publication No. 2010 / 123842 [Patent Document 7] U.S. Pat. No. 9,221,776 [Patent Document 8] U.S. Patent No. 10,208,005 [Patent Document 9] International Publication No. 2003 / 044002 [Patent Document 10] International Publication No. 2005 / 035513 [Patent Document 11] U.S. Pat. No. 9,174,928 [Patent Document 12] International Publication No. 2016 / 108975 [Patent Document 13] U.S. Pat. No. 9,892,238 Summary of the Invention

[0028] In one embodiment, a method for maximizing the selectivity (S) of an epoxidation catalyst in an ethylene oxide reactor system comprises obtaining a measured reactor selectivity (S) from an ethylene oxide production system configured to convert a feed gas comprising ethylene and oxygen to ethylene oxide in the presence of an epoxidation catalyst and a chloride-containing catalyst modifier in the ethylene oxide reactor system. meas ), the measured reactor temperature (T meas ), and one or more operational parameters.

[0029] The epoxidation catalyst contains silver and a promoting amount of rhenium (Re) and has a measured reactor selectivity (S meas ), the measured reactor temperature (T meas), and the one or more operational parameters include real-time and historical operational data points over time generated by the ethylene oxide production system. The method also includes, using a processor, (a) using the model to determine, for each time point, an optimal regulator level (M opt Model-estimated selectivity (S est ) and model-estimated temperature (T est ) is calculated. est ) and model-estimated temperature (T est ) is determined based on at least one operational parameter of the one or more operational parameters at the time point, the at least one operational parameter not including a chloride-containing modifier level, and the model is based, at least in part, on empirical historical data associated with the epoxidation catalyst, the ethylene oxide production system, or both. The method also includes, using a processor, (b) calculating, for each of the time points, the measured reactor selectivity (S meas ) and model estimation selectivity (S est ) and the measured reactor temperature (T meas ) and model-estimated temperature (T est (c) curve fitting the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve; and (d) determining the difference (ΔT) between the delta selectivity (ΔS) data points and the delta temperature (ΔT) data points to obtain a fitted curve. real-time ) and ΔT(ΔT real-time Real-time relative effective regulator levels based on real-time values ​​of

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[0037] The method further includes, using the processor, (f) displaying the actionable recommendations on a display.

[0038] In another embodiment, a method is configured to maximize the selectivity (S) of an epoxidation catalyst in an ethylene oxide reactor system, comprising: (a) using a model to determine an optimal moderator level (M) for real time and historical points over time based on at least one operational parameter at that time from an ethylene oxide production system that includes the ethylene oxide reactor system. opt Model-estimated selectivity (S est ) and model-estimated temperature (T est The one or more tangible, non-transitory, machine-readable media are provided that include instructions for calculating (a) the measured reactor selectivity (S) for each of the time points. The model is based, at least in part, on empirical historical data associated with the epoxidation catalyst, the ethylene oxide production system, or both, and the at least one operational parameter does not include a chloride-containing modifier level, and the epoxidation catalyst includes silver and a promoting amount of rhenium (Re). The one or more tangible, non-transitory, machine-readable media also include (b) a measured reactor selectivity (S) for each of the time points. meas ) and model estimation selectivity (S est ) and the measured reactor temperature (T meas ) and model-estimated temperature (T est ) and to determine the difference (ΔT) between the measured reactor selectivity (S meas ), the measured reactor temperature (T meas ), and one or more operational parameters including real-time and historical operational data points over time generated by the ethylene oxide production system at the time; (c) Fit a curve to the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve. (d) Real-time relative effective regulator levels based on fitted curves

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[0045] The one or more tangible, non-transitory, machine-readable media also include (f) instructions for displaying the executable recommendations on the display.

[0046] In a further embodiment, a system is provided that includes a reactor disposed within an ethylene oxide production system and having ethylene, oxygen, an epoxidation catalyst, and a chloride-containing catalyst modifier. The reactor is configured to convert ethylene and oxygen into ethylene oxide, and the epoxidation catalyst includes silver and a promoting amount of rhenium (Re). The system also includes a display, as well as a reactor selectivity (S) measured from the ethylene oxide production system. meas ), the measured reactor temperature (T meas ), and a data processing system configured to receive one or more operational parameters.

[0047] The measured reactor selectivity (S meas ), the measured reactor temperature (T meas ), and the one or more operational parameters include real time and historical operational data points over time generated by the ethylene oxide production system, the data processing system including a processor and, when executed by the processor, (a) using the model to determine, for each time point, an optimal regulator level (M opt Model-estimated selectivity (S est ) and model-estimated temperature (T est and one or more tangible, non-transitory, machine-readable media including instructions configured to calculate the model estimation selectivity (S est ) and temperature (T est ) is determined based on at least one operational parameter of the one or more operational parameters at the time point, the at least one operational parameter not including a chloride-containing modifier level, and the model is based at least in part on empirical historical data associated with the epoxidation catalyst, the ethylene oxide production system, or both. The one or more tangible, non-transitory machine-readable media also, when executed by the processor, (b) for each of the time points, a measured reactor selectivity (S meas ) and model estimation selectivity (S est ) and the measured reactor temperature (T meas ) and model-estimated temperature (T est(c) curve fitting the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve; (d) determining the difference (ΔT) between the delta selectivity (ΔS) data points and the corresponding delta temperature (ΔT) data points; and (e) determining the difference (ΔT) between the delta selectivity (ΔS) data points and the corresponding delta temperature (ΔT) data points to obtain a fitted curve. real-time ) and ΔT(ΔT real-time Real-time relative effective regulator levels based on real-time values ​​of

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[0054] The one or more tangible, non-transitory, machine-readable media further include instructions that, when executed by the processor, can (f) display the executable recommendation on the display.

[0055] Additional features and advantages of exemplary implementations of the present disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of such exemplary implementations. The features and advantages of such implementations may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features will become more fully apparent from the following description and the appended claims, or may be learned by the practice of the exemplary implementations as set forth hereinafter. [Brief description of the drawings]

[0056] Advantages of the present disclosure may become apparent upon reading the following detailed description and upon reference to the drawings. [Figure 1] 1 is a representative plot of catalyst selectivity (S) and catalyst chlorination effectiveness (Cleff) over time for manual lab-scale optimization of chloride modifiers. [Diagram 2] 1 is a representative plot of temperature (T) and catalyst chlorination effectiveness (Cleff) over time for manual lab-scale optimization of chloride modifiers. [Diagram 3] 3 is a representative plot of catalyst selectivity (S) and temperature (T) as a function of catalyst chlorination effectiveness (Cleff) obtained from combining the stationary points from the plots of FIGS. 1 and 2. [Figure 4] 4 is a representative plot of relative selectivity (RS) and relative temperature (RT) as a function of relative effective modifier level (RCleff) obtained from the plots of FIGS. 1-3. [Diagram 5] 1 is a representative plot of selectivity (S) as a function of catalyst chloride effectiveness (Cleff) for a compilation of various off-line laboratory tests for a given epoxidation catalyst at different operating conditions and ages. [Figure 6] 1 is a representative plot of temperature (T) as a function of catalyst chlorination effectiveness (Cleff) for a compilation of various off-line laboratory tests for a given epoxidation catalyst at different operating conditions and ages. [Figure 7] 6 is a representative plot of RS as a function of RCleff obtained from the plot of FIG. 5 with a fitted relationship, according to one embodiment of the present invention. [Figure 8] 7 is a representative plot of RT as a function of RCleff obtained from the plot of FIG. 6 with a fitted relationship, according to one embodiment of the present invention. [Figure 9] 9 is an exemplary plot of a reference curve relating RS and RT to RCleff obtained from the fitted relationships of FIGS. 7 and 8, according to one embodiment of the present invention. [Figure 10] 10 is a plot of RS vs. RT using the fitted reference curve of FIG. 9 in accordance with one embodiment of the present invention. [Figure 11] FIG. 11 is a representative plot of the slope of the plot of RS versus RT as a function of RCleff obtained from the plots of FIGS. 9 and 10 over an extended range, in accordance with one embodiment of the present invention. [Figure 12] FIG. 12 is an enlarged view of the plot of FIG. 11 around the point (0,0). [Figure 13] FIG. 1 is a schematic diagram of an ethylene oxide (EO) production system for determining maximum catalyst selectivity and providing warnings / recommendations regarding adjustment of catalyst modifier levels, according to one embodiment of the present invention. [Figure 14] 14 is a flow chart of a method used by the EO generation system of FIG. 13 to determine maximum catalyst selectivity (Sopt) and provide warnings / recommendations regarding adjustment of catalyst modifier levels, in accordance with one embodiment of the present invention. [Figure 15] 14 is a representative plot of catalyst selectivity (S) and temperature (T) as a function of days on stream using real-time operational data and model of the system of FIG. 13 in accordance with one embodiment of the present invention. [Figure 16]FIG. 16 is a representative plot of ΔS as a function of ΔT obtained from data for the recent time period covered by FIG. 15, with curves fitted to data lying on either side of the optimal modulator level (Mopt), in accordance with one embodiment of the present invention. [Figure 17] 17 is a representative plot of relative selectivity difference (RSD) as a function of relative temperature difference (RTD) from the plot of FIG. 16, with a warning of an overregulation condition, according to one embodiment of the present invention. [Figure 18] 14 is a decision tree used by the EO generation system of FIG. 13 to provide actionable guidance for adjusting catalyst modifier levels in real time, in accordance with one embodiment of the present invention. [Figure 19] 14 is a representative plot of ΔS as a function of corresponding ΔT generated by the EO generation system of FIG. 13 in accordance with one embodiment of the present invention, whereby the data lies to one side of the optimal regulator level (Mopt), with a warning of an under-regulation condition. [Figure 20] 14 is a representative plot of ΔS as a function of ΔT generated by the EO generation system of FIG. 13, whereby real-time data points are outside the prediction boundary, in accordance with one embodiment of the present invention. [Figure 21] 14 is a representative plot of ΔS as a function of ΔT generated by the EO generation system of FIG. 13 in accordance with one embodiment of the present invention, whereby trends in the data are obscured. [Figure 22] 14 is a representative plot of ΔS as a function of ΔT generated by the EO generation system of FIG. 13 in accordance with one embodiment of the present invention, whereby the slope of the curve at the real-time points is typically outside of the reference boundaries, with warnings of severe overregulation. [Diagram 23] 14 is a representative plot of ΔS as a function of ΔT generated by the EO generation system of FIG. 13 in accordance with one embodiment of the present invention, whereby the slope of the curve at the real-time points is near 0, indicating near the optimal regulator level (Mopt)t. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0057] One or more specific embodiments of the present disclosure are described below. The described embodiments are examples of the technology of the present disclosure. Moreover, in order to provide a concise description of these embodiments, all features of an actual implementation may not be described herein. It should be understood that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions will be made to achieve the developer's particular objectives, such as compliance with system-related and business-related constraints that may vary from implementation to implementation. It should be further understood that such a development effort may be complex and time-consuming, but is nevertheless a routine undertaking of design, fabrication and manufacture for those of ordinary skill in the art having the benefit of this disclosure.

[0058] When introducing elements of various embodiments of the present disclosure, the articles "a," "an," and "the" are intended to mean that there are one or more of the element. The terms "comprising," "including," and "having" are intended to be inclusive and mean that there may be additional elements other than the listed elements. Further, it is to be understood that references to "one embodiment" or "embodiments" of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.

[0059] definition As used herein, the terms "activity", "catalyst activity", and the like, are the productivity of a catalyst to make EO. Activity is often quantified using the temperature required to produce a certain amount of EO product. If the catalyst is more active, a lower temperature is required for a given level of EO production. Conversely, if the activity is lower, a higher temperature is required for a given level of EO production. References to the terms "reactor temperature", "catalyst temperature", "temperature", and the like are used interchangeably herein as a measure of catalyst activity.

[0060] As used herein, the terms “selectivity,” “reactor selectivity,” “catalyst selectivity,” and the like refer to the selectivity of ethylene (C 2 H 4 ) with competing by-products, i.e., carbon dioxide (CO 2 ) and water (H 2 O)) to the desired reaction product, ethylene oxide, and is expressed as a percentage of moles of ethylene oxide produced per moles of ethylene consumed in the reactor.

[0061] As used herein, the terms "silver-based ethylene epoxidation catalyst", "ethylene epoxidation catalyst", "epoxidation catalyst", "high selectivity epoxidation catalyst", and the like refer to a catalyst on an alumina support comprising silver in the range of 1-40 wt. % and a promoting amount of rhenium used in the epoxidation of ethylene to ethylene oxide. As used herein, the term "promoting amount" of rhenium (Re) refers to an amount of Re that effectively acts to provide an improvement in one or more of the catalytic properties of the subsequently formed catalyst when compared to a catalyst that does not contain Re. Examples of catalytic properties include, but are not limited to, selectivity, activity, and stability (i.e., loss of selectivity and activity over time). It will be understood by those skilled in the art that one or more of the individual catalytic properties may be enhanced by a "promoting amount", while other catalytic properties may or may not be enhanced, or may be decreased. It will further be understood that different catalytic properties may be enhanced at different operating conditions. For example, a catalyst having enhanced selectivity at one set of operating conditions may be operated at a different set of conditions where the improvement is shown in activity rather than selectivity.

[0062] As used herein, the term "ethylene oxide (EO) production parameter" is a measure of the extent to which ethylene oxide is produced during a process for the epoxidation of ethylene. The EO production parameter may be selected from the group including product gas ethylene oxide concentration, the change in moles of EO produced from the inlet to the outlet of the reactor, the ethylene oxide production rate, the ethylene oxide production rate per mass of silver loaded in the reactor, the ethylene oxide production rate per mass of catalyst (also known as mass work rate (WRm)) and the ethylene oxide production rate per volume of catalyst (also known as work rate (WR)). In the present invention, the preferred ethylene oxide production parameter is the work rate, although others may be selected as well without departing from the scope of the present invention. As used herein, the term "work rate" is intended to indicate the mass of EO produced per volume of catalyst per hour, and is generally expressed as kilograms (kg) per cubic meter (m) of catalyst. 3 ) / time (h), kg / m 3 It is measured in cat / h.

[0063] As used herein, "operating conditions", "conditions" and the like refer to conditions such as reactor inlet pressure, feed gas flow rate or gas hourly space velocity (GHSV), ethylene (C 2 H 4 ), oxygen (O 2 ), carbon dioxide (CO 2 ), ethane (C 2 H 6 ), methane (CH 4 ) and water (H 2 The term "operating conditions" refers to a set of measured or controlled variables, including, but not limited to, feed gas concentrations of EO, EO, and EO production parameters. Reaction temperature and moderator levels are not included in the term "operating conditions." As used herein, the term "operating parameters" refers to the above operating conditions plus reaction temperature and moderator levels.

[0064] As used herein, the terms "real-time data," "real-time data points," and the like refer to the most recent available set of operational parameters and measured selectivities. Symbols with the subscript "real-time" indicate the specific value of the time variation quantity at the real-time point. The time frame of the real-time data may be an instantaneous average, hourly average, shift average, or daily average. As used herein, the terms "historical operational data," and the like refer to a set of operational parameters and measured selectivities collected prior to the real-time data.

[0065] As used herein, the terms "modifier level", "chloride-containing modifier level" and the like are intended to refer to a process variable that is varied to change how much organic chloride is being delivered to the reactor system and catalyst. The modifier level can be any metric that directly or indirectly indicates the steady-state level of chloride control of the catalyst, such as the total or weighted total concentration of chloride species in the feed gas (i.e., modifier concentration), the chloride make-up feed rate (i.e., volumetric or mass rate), or the catalyst chloride effectiveness that takes into account the effect of hydrocarbons on the chloride control capacity. As non-limiting examples, three specific ways of defining modifier level are as follows: 1) Total weighted moderator concentration in reactor feed gas: TotCl=0.1 * [MC]+[EC]+2 * [EDC]+[VC] (Formula 1) 2) Catalyst chloride conversion effectiveness value (Cl eff ):

[0066]

number

[0067] Any of these modifier level metrics, or other metrics that represent the level of chloride control of the catalyst, can be manipulated to vary the overall chloride in the reaction system.

[0068] As used herein, the term "relative effective modulator level" (RCl eff ), "relative regulator level" etc. are the optimal regulator levels (M opt ) and is represented by the following formula: RCl eff =(M / M opt )-1(formula 3) Thereby, "M" refers to the regulator level and the subscript "opt" represents the chloride optimized regulator level.

[0069] Equation 3 is the relationship between the regulator level and RCl eff may be rearranged to calculate the chloride optimized modifier level. M opt =M / (RCl eff +1)(Formula 4)

[0070] The change required to move the regulator level to the chloride optimized regulator level (M change ), expressed as a percentage, is given by the following formula, including rearrangement of Equation 4: M change =(M opt / M-1) * 100%=(1 / (RCl eff +1) _ 1) * 100% (formula 5)

[0071] The change is also expressed as a specified percentage change (M change ) may be given as the equivalent incremental change in regulator level corresponding to

[0072] As used herein, the terms “chloride optimized modifier”, “optimized modifier”, “optimum concentration of catalyst modifier”, “optimum”, and the like are used interchangeably and refer to the optimum concentration of chloride modifier that provides maximum catalyst selectivity (S opt ) to produce a regulator level (M opt ). As used herein, the term "underregulated" is intended to indicate a catalytic condition where the chloride level in the feed gas is less than the optimum chloride (i.e., moderator) level. As used herein, the term "overregulated" is intended to mean a catalytic condition where the chloride level in the feed gas is greater than the optimum chloride (i.e., moderator) level.

[0073] "Relative selectivity" (RS) is the measured selectivity (S meas ) and optimal regulator level (M opt ) selectivity (S opt ) and is expressed by the following formula: RS=S meas -S opt (Formula 6)

[0074] "Relative temperature" (RT) is the temperature measured (T meas ) and optimal regulator level (M opt ) at temperature (T opt ) and is expressed by the following formula: RT=T meas -T opt (Formula 7)

[0075] "Delta selectivity" (ΔS) is the measured selectivity S meas and the model predicted value of selectivity S est and is expressed by the following formula: ΔS=S meas -S est (Formula 8)

[0076] "Delta temperature" (ΔT) is the measured temperature (T meas ) and the model predicted value of temperature (T est ) and is expressed by the following formula: ΔT=T meas -Test (Formula 9)

[0077] The model optimizes chloride selectivity and temperature versus feed gas composition (e.g., O 2 , C 2 H 4 , CO 2 , C 2 H 6 , C.H. 4 , H 2 The model may be any suitable model that predicts the effect of changes in the GHSV, pressure and EO generation parameters.

[0078] The "relative selectivity difference" (RSD) is expressed by the following formula: RSD = ΔS - ΔS opt (Formula 10) Here, ΔS opt is the maximum ΔS value along the fitted curve of ΔS vs. ΔT (see, for example, FIG. 16), and RSD is the maximum selectivity (S opt ) is defined to be 0.

[0079] "Relative Temperature Difference" (RTD) is expressed by the following formula: RTD = ΔT - ΔT opt (Formula 11) Here, ΔT opt is the maximum ΔS (i.e., ΔS opt ) and RTD is defined to be 0 at the optimum point.

[0080] Advantages of the System and Method The most common and basic method of optimizing the moderator level of a high selectivity epoxidation catalyst (e.g., epoxidation catalyst with silver and promoting amount of Re) is for the operator of the EO production system in the EO plant to manually change the moderator level step by step. As will be appreciated by those skilled in the art, there are many disturbances and changes in operational objectives in normal plant operation based on demand resulting in fluctuating system conditions. Thus, monitoring selectivity and temperature as a function of moderator (i.e., chloride) in the plant would result in undesirable noise due in part to condition variations that are difficult to separate from the true effects of moderator level changes. In order to accurately and reliably determine maximum catalyst selectivity, the effects of various operational parameters of the plant (e.g., temperature, EO production parameters, gas hourly space velocity, pressure, feed composition, moderator level, etc.) should be considered. The technology disclosed herein is not limited to changing only one operating condition, while all other operating conditions remain substantially constant. By using the processes and methods disclosed herein, the variations in operating conditions can be accounted for, thereby allowing the effects of changes in moderator levels to be clearly observed more reliably and over a longer period of time.

[0081] As discussed above, certain existing techniques for optimizing catalyst selectivity require accurate measurements of gas phase chlorides in industrial EO plants to utilize a linear or power equation best fit curve of moderator concentration as a function of temperature, or an effective ratio of a weighted sum of moderator concentration to a weighted sum of hydrocarbon concentrations. Other techniques determine the change of only one parameter at a time, such as temperature or moderator concentration, to achieve a desired EO production parameter or another target parameter while other parameters remain constant.

[0082] However, unlike existing techniques, the present invention surprisingly finds that it is not necessary to precisely measure gas phase chloride in an EO plant environment to optimize catalyst selectivity. Rather, it is sufficient to effectively vary the chloride feed rate without relying on gas phase chloride analysis. Specifically, the techniques disclosed herein use the catalyst performance itself to account for the lag between changing the moderator level and its effect on catalyst performance. This avoids the potential problem of lag in chloride equilibrium on the catalyst surface, which could otherwise confound optimization.

[0083] Moreover, in combination with taking into account the variations in the operating conditions of the EO production plant (e.g., gas hourly space velocity (GHSV), EO production parameters, feed gas composition, pressure) using historical operating data of the EO plant run, e.g., collected for more than 7 days, and reference data collected during offline catalyst testing (e.g., laboratory or pilot plant testing) or from past EO plant runs, the methods disclosed herein provide a robust, efficient and accurate method for maximizing catalyst selectivity. Thus, disclosed herein are specific to high selectivity epoxidation catalysts and utilize models and reference data obtained during offline testing or from past EO plant runs to maximize catalyst selectivity (S opt Optimal regulator levels (M opt It is a technology that takes into account a variety of potentially variable operational parameters to accurately and reliably determine the

[0084] In particular, the present invention is generally directed to a robust system and method for accurately and reliably determining optimal catalyst modifier levels in real time that maximize catalyst selectivity in the presence of operational changes in an EO production system, and providing actionable guidance for adjusting the modifier levels so that catalyst performance is optimized. The systems and methods disclosed herein use information derived from models and decision trees in combination with empirical historical data generated by the EO production system over time, and catalyst-specific reference data routinely obtained during catalyst development and support, or from past EO plant operations. Unlike existing modifier optimization techniques, the systems and methods disclosed herein do not rely on monitoring gas-phase modifier concentrations in the reactor or feed gas, which can be difficult to obtain reliably and may not represent the concentration of catalyst modifier on the surface of the catalyst. Rather, the disclosed systems and methods rely on the overall impact of the catalyst modifier (i.e., on the surface of the catalyst) on catalyst performance. Furthermore, the methods not only provide directional advice, but also provide the specific magnitude of modifier level change required to reach the optimum. For example, by using the model, variability from differences in operating conditions (e.g., EO production parameters, reactor feed gas composition, pressure, or gas hourly space velocity) that may otherwise confound the determination of optimal catalyst modifier levels is removed.

[0085] As mentioned above, certain ethylene epoxidation catalysts are subject to degradation-related performance degradation during normal operation of a system used to produce ethylene oxide (EO). Catalyst degradation is observed by a decrease in catalyst activity and selectivity over time. Thus, to compensate for the reduced catalyst activity, the temperature is increased in the reactor in which the epoxidation takes place. The temperature in the reactor changes over time with catalyst degradation. Even for a given catalyst age, temperature requirements change as conditions such as desired EO production parameters or feed composition change. In plant operations, both changes related to temperature declines and changes related to changes in operating conditions are common, and it is advantageous to consider both types of changes in any scheme to optimize moderator levels. Incorporating the effects of catalyst degradation and changes in operating conditions over longer operating times improves accuracy compared to existing techniques. The present invention considers both types of changes to provide the maximum catalyst selectivity (S) at any catalyst age or operating conditions. opt Optimal regulator levels (M opt ) accurately and reliably in real time.

[0086] Reference curve Optimal Regulator Level (M opt ) depends on the epoxidation reaction conditions and the type of catalyst used. Highly selective epoxidation catalysts, such as silver-based catalysts with promoting amounts of rhenium (Re), require a particular moderator level (M opt ) with maximum selectivity (S opt ) The curves showing catalyst selectivity as a function of modifier level therefore have complex shapes including selectivity maxima indicating that selectivity can vary considerably with relatively small changes in catalyst modifier level, as disclosed in EP 0 352 850 A1.

[0087] The behavior of highly selective epoxidation catalysts under various reaction conditions can be determined from reference curves generated using offline test data collected during catalyst development and evaluation or routinely from EO plant operations. These reference curves can be used to identify the moderator level that results in maximum catalyst selectivity. For example, the offline test data can be used to obtain reference curves that define catalyst performance versus moderator level relationships. These reference curves can be used in conjunction with the techniques disclosed herein to determine the relative effective moderator level (RCl) of a catalyst in real time during operation of an EO production system. eff ) can be determined efficiently and effectively.

[0088] To facilitate the description of the present invention, the following is a brief description of how the reference curves used by the disclosed embodiments are obtained. Figures 1 and 2 show plots 10 and 12, respectively, showing the response of catalyst selectivity (%) and temperature (°C) as the moderator level is changed over time for an EO production system at constant operating conditions and constant EO production parameters. Plots 10 and 12 were generated using microreactor data obtained during the optimization of a highly selective epoxidation catalyst (i.e., a silver-based catalyst with a rhenium promoter). It should be understood that while plots 10 and 12 were generated using data from microreactor testing, the data may also be generated in a commercial EO plant. As shown in Figures 1 and 2, the moderator level (i.e., catalyst chloride effectiveness value, Cl eff ) were manually stepped approximately once per day. A period of time was allowed for the effect of each modifier step change on the selectivity of the catalyst to stabilize. Once stabilized, stabilized data points 14 and 16 for selectivity and temperature, respectively, were extracted at each modifier level 18.

[0089] The extracted stable data points 14 and 16 may be used to generate additional plots of catalyst selectivity and temperature as a function of moderator level 18. For example, FIG. 3 shows a plot 20 of selectivity and temperature as a function of moderator level 18 generated using the respective extracted stable data points 14 and 16. Plot 20 shows the trend of the behavior of a high selectivity epoxidation catalyst as the moderator level is changed. For example, as the moderator level increases at a constant target EO production parameter, the selectivity of the catalyst (e.g., data point 14) passes through a maximum point 34 and the temperature (e.g., data point 16) decreases. In addition, plot 20 shows performance at an optimal value (e.g., at or near selectivity maximum point 34), above the optimal value (i.e., over-adjusted), and below the optimal value (i.e., under-adjusted). In the illustrated plot 20, if the extracted stable data points 14 and 16 are to the left of maximum point 34, the catalyst is under-adjusted and the moderator level can be increased to improve the selectivity of the catalyst. Conversely, if the extracted stable data points 14 and 16 lie to the right of the maximum point 34, the catalyst is in an over-regulated state and the regulator level can be reduced to improve the selectivity of the catalyst.

[0090] An alternative depiction of the extracted stable data points 14 and 16 for selectivity and temperature, respectively, trending around maximum point 34, is shown in plot 24 of FIG. 4. Data points 36 and 38 for relative selectivity (RS) and relative temperature (RT), respectively, in plot 24 are plots of the extracted stable selectivity and temperature data points 14 and 16 and the optimum modifier level (M opt ) and temperature values ​​using Equations 6 and 7, respectively. In this particular plot, each data point 36 and 38 represents the relative effective modifier level 26 (RCl eff ) is plotted as a function of RCl eff 26 indicates the degree to which the catalyst is over- or under-regulated. For example, RCl eff =-0.2 indicates a 20% underregulated accommodation state, RCl eff= +0.15 indicates a 15% overregulated regulatory state.

[0091] The selectivity curves associated with the highly selective epoxidation catalysts shown in Figures 3 and 4 have a pronounced maximum (e.g., maximum point 34) that indicates the optimum catalyst modifier level to achieve maximum catalyst selectivity for a given set of reaction conditions. In addition, the amount of modifier required to achieve maximum selectivity typically changes as a function of temperature as the catalyst ages or as operating conditions change. Temperature is a function of the optimum modifier level (M opt ), but other factors such as EO production parameters, feed gas composition, and other operating conditions also lead to changes in the optimal moderator concentration. For example, changes in work rate or reactor inlet CO 2 As the levels change, the optimal regulator level (M opt ) also changes.

[0092] Thus, as these conditions change, it may be necessary to adjust (ie, increase or decrease) the moderator concentration in order to maintain maximum catalyst selectivity.

[0093] 1-4 show the behavior of a single optimization example for a given catalyst type, age, and set of operating conditions. However, similar curves can be generated from data collected in a laboratory during catalyst development and evaluation for a variety of different catalyst ages and different operating conditions. For example, FIGS. 5 and 6 show plots 46 and 48 of selectivity versus temperature, respectively, for different ages and a wide range of operating conditions, e.g., EO production parameters, GHSV, O 2 Supply gas concentration, C 2 H 4 Feed concentration, CO 2 The catalytic chloride effectiveness (Cl) was generated by compiling data 50 and 52 from various tests associated with a given catalyst at feed concentration and reactor inlet pressure, respectively. eff) as a function of temperature. As shown in respective plots 46 and 48, the selectivity, temperature, and catalytic chlorination effectiveness values ​​vary over very wide ranges (e.g., selectivity greater than 10%, above 50°C, and a factor of 10 in catalytic chlorination effectiveness). However, what is surprising is that these data 50 and 52 are presented in relative terms (e.g., RS, RT, and RCl). eff ), the wide range of performance data falls into a much narrower range, as shown in Figures 7 and 8.

[0094] For example, Figs. 7 and 8 show the RCl calculated from Eq. 3 for various offline laboratory tests under different conditions, as shown in Figs. 5 and 6. eff 5A and 5B are representative reference plots 56 and 58 of relative selectivity (RS) deviation from optimum in percent (%) and relative temperature (RT) compared to optimum point 34 in degrees Celsius (°C), calculated from equations 6 and 7, respectively, as a function of Relative Selectivity (RS) deviation from optimum in percent (%) and relative temperature (RT) compared to optimum point 34 in degrees Celsius (°C), calculated from equations 6 and 7, respectively. By definition, RS and RT are both at the optimum RCl, which is 0.0. eff As shown, the fitted selectivity reference curve 60 and fitted temperature reference curve 64 can represent a wide range of experimental data 62 and 68, respectively, that have been examined. The fitted reference curves 60 and 64 can be determined using a wide range of readily available statistical curve fitting techniques known to those skilled in the art, constrained to pass through the point (0.0,0.0).

[0095] A wide variety of laboratory data 50 and 52 was collected as shown in Figures 5 and 6 to generate the representative reference plots 56 and 58 of Figures 7 and 8, although it will be understood that this is not necessary to practice the methods disclosed herein. The selectivity and temperature data collected over a wide variety of operating conditions and catalyst ages surprisingly yielded results in relative terms (RS and RT vs. RCl). eff), will fall on the fitted selectivity reference curve and on the fitted temperature reference curve, so it is not necessary to generate such extensive data sets in the laboratory during catalyst development to arrive at a representative reference plot. For example, an EO plant operator can operate an EO plant early in a catalyst run at a particular set of operating conditions in such a way that a single set of selectivity and temperature data is collected over a range of moderator levels that encompasses under-adjustment, optimal adjustment, and over-adjustment. The EO plant operator can use these data to generate a representative reference plot for use during subsequent runs of the catalyst.

[0096] 9 is a reference plot 70 which is a replot of the fitted reference curves 60 and 64 shown in FIGS. 7 and 8, without all of the underlying data points in order to show the relevant trends in a single plot. The reference plot 70 shows the relative selectivity (RS) and relative temperature (RT) of RCl. eff 26. In this plot, by definition, the maximum value 72 of the fitted selectivity curve 60 is the maximum value 72 of the RCl eff 0. As explained in more detail below, these curves can be used as useful benchmarks for optimizing regulator levels in real-time operation of an EO generating system.

[0097] An alternative view of the fitted reference curves 60 and 64 is shown in FIG. 10, which shows a plot 73 of the data as RS versus RT. In this view, data points to the left of the maximum 72 are over-adjusted and data points to the right of the maximum 72 are under-adjusted. Each data point along the curve 74 has an associated RCl, as obtained from FIG. eff . Examination of the slope 76 of the fitted curve 74 in this depiction can provide valuable insight into the state of catalyst regulation. For data points to the left of the maximum 72, such as data point 78, the slope 76 of the line is positive, which corresponds to over-regulation. For data points to the right of the maximum 72, such as data point 80, the slope 76 of the line is negative, which corresponds to under-regulation.

[0098] When a data point is close to the maximum value 72, the slope 76 will be close to zero, which corresponds to optimal tuning. In this manner, the slope 76 of the curve 74 can be used to determine the tuning state of the catalyst. In practice, the slope 76 of the RS vs. RT curve 74 at a given data point can be determined by taking the derivative of the fitted function.

[0099] FIG. 11 is associated with each data point from curve 74 of FIG. 10, and RCl eff A wide range of RCl eff Slope of RS vs. RT curve 74 as a function of 26

[0100]

number

[0101]

number

[0102] FIG. 12 is an enlarged view of the plot of FIG. 11 around point 0,0, which is the RCl that lies from about -0.5 to about +0.3. effThe slope of the RS vs. RT curve over the region of interest at 26 (e.g., region 87 in FIG. 11 ).

[0103]

number

[0104]

number

[0105]

number

[0106]

number

[0107] The data used to generate the reference curves described above with reference to Figures 7-12 may be collected offline during testing of catalysts for development or support. However, as should be understood, these same reference curves may also be generated during commercial plant operation. As discussed above, the reference curves can cover catalyst behavior over a wide range of conditions, providing an effective and efficient method for moderator optimization compared to existing techniques. Additional catalyst-specific reference curves for different catalyst types can be utilized and applied as needed.

[0108] A particular advantage of the present invention is that, instead of requiring a complete optimization procedure in a plant during each run as per the teachings of the prior art, the present invention allows a reference curve previously generated in a laboratory scale or smaller pilot plant test to be utilized in informing the optimization procedure in a subsequent larger run. The application of the reference curve generated in such a test provides the plant operator with greater efficiency and lower costs than if they had to rely on conducting experiments during commercial runs, particularly if such experiments would need to be re-run during commercial runs whenever there are changes in operating conditions or temperatures in the plant. By obtaining a reference curve in advance in a laboratory scale or smaller pilot plant test for a given catalyst composition, such a curve can be applied in future runs of the catalyst composition for larger runs across multiple plants.

[0109] EO Generation System With the above in mind, FIG. 13 illustrates an ethylene oxide production system 100 that can utilize the disclosed process to determine optimal moderator levels for maximum catalyst selectivity in real time, in accordance with one embodiment of the present invention.

[0110] In the illustrated embodiment, the system 100 includes an epoxidation reactor system 102, a carbon dioxide (CO 2 The epoxidation reactor system 102 includes an ethylene oxide (EO) separation system 104, an ethylene oxide (EO) separation system 106, and a control system 108. The epoxidation reactor system 102 may include one or more reactors in parallel or series. In operation, the epoxidation reactor system 102 generates and supplies ethylene 124, a catalyst modifier 126, oxygen (O 2 The catalyst receives a feed gas 120 including a chloride-containing modifier 126 (e.g., a chloride-containing modifier), such as methyl chloride, ethyl chloride, ethylene dichloride, vinyl chloride, and combinations thereof. 1 ~C 3 These may include, but are not limited to, chlorohydrocarbons.

[0111] As shown in Figure 13, a feed gas 120 is fed to an epoxidation reactor system 102 via a reactor inlet 132. In the epoxidation reactor system 102, the ethylene 124 and oxygen 128 in the feed gas 120 react in the presence of an epoxidation catalyst 134 to produce a product gas 138. The product gas 138 is comprised of EO, unreacted ethylene 124 and oxygen 128, a catalyst modifier 126, and various by-products of the epoxidation reaction (i.e., CO 2 and water (H 2 O), diluent gases and other impurities. The epoxidation process in reactor system 102 disclosed herein may be carried out under a wide range of operating conditions that may vary widely between different ethylene oxide plants using system 100, depending, at least in part, on the initial plant design, subsequent expansion projects, feedstock availability, type of catalyst used, process economics, and the like. Examples of such operating conditions include, but are not limited to, reactor inlet pressure, gas flow through reactor system 102 (commonly expressed as gas hourly space velocity or "GHSV"), feed gas composition, and ethylene oxide production parameters (commonly described in terms of work rate).

[0112] To achieve the desired commercial ethylene oxide production rate, the epoxidation reaction is typically carried out at a reaction temperature of 180° C. or more, or 190° C. or more, or 200° C. or more, or 210° C. or more, or 225° C. or more. Similarly, the reaction temperature is typically 325° C. or less, or 310° C. or less, or 300° C. or less, or 280° C. or less, or 260° C. or less. The reaction temperature may be 180° C. to 325° C., or 190° C. to 300° C., or 210° C. to 300° C. It should be noted that the term "reaction temperature" as used herein refers to any selected temperature that directly or indirectly indicates the catalyst bed temperature.

[0113] For example, the reaction temperature can be the catalyst bed temperature at a particular location within the catalyst bed, or the numerical average of several catalyst bed temperature measurements made along one or more catalyst bed dimensions (e.g., along the length). Alternatively, the reaction temperature can be, for example, the gas temperature at a particular location in the catalyst bed, the numerical average of several gas temperature measurements made along one or more catalyst bed dimensions, the gas temperature measured at the outlet of the epoxidation reactor, the numerical average of several coolant temperature measurements made along one or more catalyst bed dimensions, or the coolant temperature measured at the inlet or outlet of the epoxidation reactor or in the coolant circulation loop. One example of a well-known device used to measure the reaction temperature is a thermocouple.

[0114] The epoxidation processes disclosed herein are typically run at reactor inlet pressures of 1000-3000 kPa, or 1200-2500 kPa (absolute). Various well-known devices can be used to measure the reactor inlet pressure, such as pressure indicating transducers, gauges, and the like. It is within the ability of one of ordinary skill in the art to select a suitable reactor inlet pressure, taking into account, for example, the particular type of epoxidation reactor, the desired productivity, and the like.

[0115] Gas flow through the epoxidation reactor is expressed in units of gas hourly space velocity ("GHSV"), which is the volumetric flow rate of the feed gas 120 at standard temperature and pressure (e.g., 0° C., 1 atm) divided by the catalyst bed volume (i.e., the volume of the epoxidation reactor system 102 containing the epoxidation catalyst 134). GHSV represents how many times per hour the feed gas 120 would displace the catalyst volume in the reactor system 102 if the feed gas 120 was at standard temperature and pressure (i.e., 0° C., 1 atm). Typically, for a gas phase epoxidation process, the GHSV is about 1,500 to 10,000 per hour.

[0116] As discussed above, the production rate of ethylene oxide in the reactor system 102 is typically described in terms of EO production parameters such as work rate, which refers to the amount of ethylene oxide produced per unit volume of catalyst per hour. In general, for a given set of operating conditions, increasing the reaction temperature at those conditions will increase the work rate and result in increased ethylene oxide production. However, this temperature increase often reduces catalyst selectivity and may accelerate catalyst degradation. Alternatively, as the epoxidation catalyst undergoes natural catalyst degradation over time, the work rate naturally decreases for a given reaction temperature. Under such circumstances, the reaction temperature is increased to maintain the work rate at the required value. Typically, the work rate in most plants is about 1 m of catalyst per hour. 3 Approximately 50 to 400 kg per m 3 / h), or about 120 to 350 kg / m 3 / h ethylene oxide. One of ordinary skill in the art having the benefit of this disclosure will be able to select appropriate operating conditions, such as feed gas composition, reactor inlet pressure, GHSV, and work rate, depending on, for example, plant design, equipment constraints, age of the epoxidation catalyst, etc.

[0117] As described above, the reactor system 102 generates and processes EO, unreacted ethylene 124 and oxygen 128, catalyst modifier 126, and various by-products of the epoxidation reaction (i.e., CO 2 and water (H 2The epoxidation reactor system 102 produces a product gas 138 that is a mixture of ethylene 124 and oxygen 128, diluents, and other impurities. The product gas 138 exits the epoxidation reactor system 102 via a reactor outlet 140 and is fed to the EO separation system 106. In the EO separation system 106, the EO is separated from the product gas 138 by any suitable separation technique. For example, in the illustrated embodiment, an extraction fluid 142, such as water, may be used to separate the EO from the product gas 138. The extraction fluid 142 removes the EO from the product gas 138 to produce an EO-enriched stream 146 having EO. The EO-enriched stream 146 exits the EO separation system 106 through a first outlet 150 (e.g., an EO outlet) and may be further processed and used to provide products such as glycols (e.g., ethylene glycol, diethylene glycol, triethylene glycol, etc.) via catalytic or non-catalytic hydrolysis. The unreacted ethylene 124 and oxygen 128, by-products (CO 2 and H 2 Overhead gas 148, containing CO2, HO, and other diluents and impurities, exits EO separation system 106 through a second outlet 152 and is recycled to reactor system 102 (e.g., via recycle gas stream 130). A compressor 158 or other suitable device may be used to facilitate transport of overhead gas 148 through system 100. In the illustrated embodiment, a first recycle gas stream 160 exiting compressor 158 is sent to feed gas 120. A portion 162 of first recycle gas stream 160 is sent to feed gas 120. 2 to a separation system 104, where CO 2 is separated from the first recycle gas stream 160 to produce CO 2 164 and a second recycle gas stream 168 are produced. The second recycle gas stream 168 is combined with the first recycle gas stream 160 to produce a recycle mixed gas stream 130, which contains ethylene 124, catalyst modifier 126, and oxygen (O 2 ) 128 to form the feed gas 120, which is fed to the reactor system 102. In this manner, unreacted ethylene and oxygen in the product gas 138 can be returned to the reactor system 102, thereby improving the overall efficiency of the EO production system 100.

[0118] As discussed above, catalyst modifiers (e.g., modifier 126) play an important role in maintaining the activity and selectivity of the catalyst (e.g., epoxidation catalyst 134) used to produce EO. When using a highly selective epoxidation catalyst, such as a silver-based catalyst with a promoting amount of rhenium, maximum catalyst selectivity can be obtained within a narrow modifier level in the feed gas (e.g., feed gas 120). However, it is not always possible to obtain maximum catalyst selectivity (S opt ) is the optimal regulator level (M opt ) is not fixed but varies based on reaction temperature and operating conditions. As catalyst performance degrades over time, the reaction temperature is increased to improve catalyst performance and maintain a constant rate of EO production. Thus, the level of moderator 126 in the feed gas 120 is typically adjusted along with the reaction temperature and reaction operating conditions, e.g., feed concentration or EO production parameters (e.g., work rate), to operate the catalyst 134 at maximum selectivity (S opt As described in more detail below, the technology disclosed herein uses a combination of real-time data, model data, reference data (e.g., the reference data in Figures 7-12), and empirical historical data to determine maximum catalyst selectivity (S opt Optimal regulator levels (M opt The present technique also determines the optimal regulator level (M) of regulator 126 in real time with improved accuracy and reliability compared to existing techniques. opt This article provides practical guidance for adjusting the

[0119] The EO generation system 100 of FIG. 13 includes one or more sensors 170 and / or an analyzer system 172 that measure and monitor one or more operational parameters of the system 100 in real time. The analyzer system 172 may include one or more analyzers that analyze the feed gas 120, the product gas 138, or both. For example, during operation, the analyzer system 172 receives a portion 176 of the feed gas 120 and measures the concentration of the moderator 126 (i.e., chloride) and other components in the feed gas 120. In certain embodiments, the analyzer system 172 may receive a portion 178 of the product gas 138 and measure the concentration of EO and other components in the product gas 138. The analyzer system 172 may include a gas chromatograph (GC), a mass spectrometer (MS), or any other suitable analytical tool for analyzing the feed gas 120 and / or the product gas 138, and combinations thereof. In the illustrated embodiment, one or more sensors 170 may be positioned within the reactor system 102 to monitor the temperature of the coolant supplied to the reactor 102 and / or the reactant gas temperature at one or more locations along the reactor 102. The sensors 170 may be positioned within the shell of one or more reactors, within one or more reactors themselves, within the coolant circulation loop, within selected catalyst tubes, and combinations thereof. In one particular embodiment, the sensor 170 may be positioned at the reactor outlet 140. Thus, the sensor 170 may measure and monitor the temperature of the product gas 138 exiting the reactor 102. The temperature of the product gas 138 may also provide insight into the temperature within the reactor system 102 and, therefore, the activity of the catalyst 134.

[0120] Control System Using data collected in real time from the sensor 170 and the analyzer 172, the performance of the catalyst 134 and the level of the modifier 126 are determined to be the maximum catalyst selectivity (S opt) at the operating conditions of the reactor system 102 without requiring an operator to perform several manual steps on the level of the moderator 126 in the reactor system 102. The data 180 may include a number of measurements related to the operation of the reactor system 102 (e.g., chloride concentration in the feed gas 120 or the rate of addition of the moderator 126, EO concentration in the product gas 138, measured selectivity, reaction temperature, pressure, gas hourly space velocity (GHSV), feed gas composition, EO production parameters, etc.). The data processing system 182 may use the data 180 to determine whether the catalyst selectivity (S) at the operating conditions of the reactor system 102 is optimal or near optimal for the level of the moderator 126 in the reactor system 102 without requiring an operator to perform several manual steps on the level of the moderator 126 in the reactor system 102. opt ) is maximized by the optimal level of regulator (M opt In addition, as described in more detail below, the data processing system 182 can advantageously determine the optimal modifier level (M) 126 for the catalyst 134 without relying on accurate and precise monitoring of the concentration of the modifier 126 in the feed gas 120 and / or on the surface of the catalyst 134. opt ) and maximum selectivity (S opt In certain embodiments, where reliable regulator measurements are available, these measurements can be used in combination with the methods disclosed herein to determine optimal regulator levels (M opt ) can be determined.

[0121] The data processing system 182 may include a microprocessor (μP) 184, a memory 186, a storage device 190, and / or a display 192. The memory 186 is used to operate the system 100 and to calculate the maximum catalyst selectivity (S opt Optimal regulator level (M opt), determine maximum catalyst selectivity, determine relative effective modifier levels for the catalyst, trigger alerts related to catalyst performance, and provide actionable guidance / recommendations for retargeting, which may include adjusting modifier levels to achieve maximum catalyst selectivity. In certain embodiments, the one or more sets of instructions may direct the system 100 to adjust the level of modifier 126 (e.g., total weighted modifier concentration, make-up modifier feed rate, or catalyst chloride effectiveness value (i.e., Cl) based on the recommendations. eff )). For example, in one particular embodiment, the control system 108 includes a feedback control element 196 that can receive instructions to automatically adjust the modifier concentration or feed rate of the modifier 126. The feedback control element 196 can send a signal 198 to a valve that controls the flow of the modifier 126, thereby adjusting the amount of the modifier 126 in the feed gas 120.

[0122] The memory 186 may include instructions for predicting optimized performance of the catalyst 134 by using a model that takes into account changes in the operational parameters of the system 100. Advantageously, the model does not require a level of the moderator 126 in the feed 120 and / or on the surface of the catalyst. That is, the model determines whether changes in pressure, gas hourly space velocity, EO production parameters, feed gas composition, and optionally catalyst age affect the optimal moderator level (M opt ) versus catalyst selectivity and temperature. Thus, deviations in catalyst selectivity and temperature from the model estimates are related to changes in relative effective moderator levels. The model estimates the effect of feed gas composition (e.g., O) versus chloride optimized selectivity and temperature, preferably as a function of catalyst age. 2 , C 2 H 4 , CO 2 , C 2 H 6 , C.H. 4 , H 2The model may be any suitable model that predicts the effects of the EO, GHSV, pressure, and EO production parameters. The model may be specific to the catalyst 134 and provided by the catalyst supplier. Thus, in one embodiment, the model is provided by the catalyst supplier that provided the catalyst, and the model is incorporated into the systems and methods disclosed herein. In another embodiment, the operator of the catalyst 134 in the EO production system 100 may develop the model based on data collected during process operation. As non-limiting examples, the model may be an empirical statistical model, a multivariate model, a kinetic model, a neural network, or any other suitable model that captures the effects of the operating conditions of the system 100, the catalyst 134, and preferably catalyst age. Examples of such models can be found in "An Experimental Study of the Kinetics of Selective Oxidation of Ethene over a Silver on α-Alumina Catalyst", PC Borman and KR Westerterp, Ind. Eng. Chem. Res, 1995, 34, 49-58 and "Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor", G Zahedi, A Lohi, KA Mahdi, Fuel Processing Technology, 2011, 92, 1725-1732. WO 2016 / 108975(A1) demonstrates an example of a catalyst model and a method for generating it in Experiment 1 in paragraphs

[0077] to

[0080] . In certain embodiments, continuous online model recalibration techniques can be used. As a non-limiting example, continuous online model recalibration techniques can include weighted reestimation of model parameters or other suitable recalibration techniques as understood by those skilled in the art. As will be appreciated, these techniques may be applied by one of ordinary skill in the art through the collection and adaptation of performance data for the highly selective epoxidation catalyst of interest.

[0123] The memory 186 may store models, decision trees, and any other information that may be used to determine the relative effective modifier levels and modifier optimization performance of the catalyst, as well as to provide actionable guidance / recommendations regarding the direction and amount that modifier levels should be adjusted to achieve maximum catalyst selectivity. The memory 186 may also store instructions for generating visualizations 192 to display for an operator of the system 100 related to catalyst performance, modifier levels, system performance, and system maintenance. The visualizations include, but are not limited to, plots, data confidence levels, alerts, recommendations, measurements, and system parameters, among others.

[0124] To process the data 180, the processor 184 may execute instructions stored in the memory 186 and / or the storage device 190. For example, the instructions may cause the processor 184 to estimate changes in the moderator level (M) as a function of changes in temperature (i.e., reaction temperature) or feed composition, and to estimate the observed moderator level as an optimal moderator level (M opt) and a reference curve to determine the effect of the moderator level on catalyst performance, independent of the concentration of the moderator in the feed gas 120. The present invention relies on being able to effectively vary the rate of chloride (i.e., moderator) addition, which is a universal requirement for operation of the system 100 and is easier than having to accurately monitor the chloride concentration in the feed gas 120. However, as long as an accurate and reliable concentration of chloride in the feed gas 120 is measured, this value can be used to determine the effect of the moderator concentration on catalyst selectivity. In certain embodiments, the instructions may cause the processor 184 to apply data pre-processing / cleaning steps to average or smooth the data, remove outliers, perform data imputation, etc. Thus, the memory 186 and / or storage device 190 of the data processing system 182 may be any suitable article of manufacture capable of storing instructions. As non-limiting examples, memory 186 and / or storage 190 may be read-only memory (ROM), random-access memory (RAM), flash memory, optical storage media, hard disk drives, cloud storage, or other storage media.

[0125] The data processing system 182 may transmit data (e.g., measurements, plots, operating parameters, operating conditions, etc.) to an external device (e.g., a remote display, a cell phone, a tablet, a laptop, an electronic data management system, etc.) that may be readily accessible to an EO plant operator. The display 192 may be any suitable local or remote electronic display capable of displaying information related to the operation of the system 100 (e.g., catalyst selectivity plots, temperature plots, work rate plots, moderator concentration plots, moderator optimization guidance plots, warnings, recommendations, confidence levels, system parameters, or any other suitable information and combinations thereof). In certain embodiments, the ... opt Optimal regulator level (M optInformation obtained from modeling runs, ad-hoc assertions from operators, empirical historical data (e.g., historical operational data), and reference data (e.g., data generated during catalyst development and testing) can be used in combination with data 180 (e.g., real-time data) to determine the estimated catalyst performance (i.e., the estimated catalyst performance). In one embodiment, data processing system 182 can retrieve data (e.g., reference data, empirical historical data, etc.) stored on a cloud, server, and / or a third party system to which data processing system 182 is authorized to access.

[0126] As discussed above, data 180 from the system 100 can be combined with a model and analyzed to determine the relative effective moderator level (RCl) of the catalyst 134 without relying on precise monitoring of the moderator concentration in the feed gas 120. eff ) and performance. For example, the model can determine the estimated chloride optimized selectivity and temperature variation of the catalyst as the operating conditions of the system 100 (e.g., EO production parameters, pressure, gas hourly space velocity (GHSV), feed composition, etc.) change. The model can also determine the effect of long-term degradation or catalyst aging. As described in more detail below, at least a portion of the data 180 is used in combination with model data and reference data stored in the data processing system 182 to determine the effect of the level of the modifier 126 on the selectivity and activity of the catalyst. As discussed above, the catalyst surface concentration (coverage) of the modifier 126 affects catalyst performance. However, it is not practical to measure the concentration of the modifier 126 on the surface of the catalyst. Existing techniques rely on measuring the concentration of the modifier 126 in the feed gas 120, which does not necessarily represent the concentration of the modifier 126 on the surface of the catalyst 134. Thus, by not relying on a measured concentration of the moderator 126 in the feed gas 120 or on the surface of the catalyst 134, the technology disclosed herein is able to predict the optimal moderator level (M) under any given set of operating conditions in real time, as compared to existing technologies. opt ) and maximum catalytic selectivity (S opt) is a more robust, reliable, and accurate method for determining the optimal value (M opt ) without the need to find the maximum catalytic selectivity (S opt ) and provide actionable guidance / recommendations for adjusting regulator levels to achieve

[0127] By using real-time collected data 180, system historical data (i.e., empirical data), model data, and reference data relating to the effect of changes in system 100 operating conditions on optimal moderator concentration and catalyst performance, the moderator optimization techniques disclosed herein can be tailored to a particular system and catalyst. The models disclosed herein can learn from historical data obtained throughout the operation of the epoxidation system 100 about the operational parameters, particularly the moderator levels, that provide maximum catalyst selectivity. That is, the models can be fine-tuned by using real-time data 180 collected over time (e.g., days, weeks, months, years) to optimize the optimal catalyst selectivity (S opt Optimal regulator level (M opt ) can provide reliable and accurate estimates of the moderator concentration. In certain embodiments, reference data obtained from offline microreactor testing of a desired catalyst during catalyst development and support, or from EO plant operations, may be part of the historical data that the model can use to fine-tune the estimates. Thus, combining real-time data 180 with historical and reference data (e.g., the reference data of Figures 7-12) facilitates determining the moderator concentration adjustments that maximize catalyst selectivity for a given system and catalyst. Thus, the disclosed system 100 reduces the complexity of existing moderator / catalyst optimization techniques that rely on monitoring moderator concentration and provides a robust system with improved accuracy and reliability of real-time moderator optimization compared to existing techniques.

[0128] Optimal Regulator Level (M opt ) How to determine The system 100 can be used to measure the catalytic selectivity (S opt ) (e.g., the selectivity of the catalyst 134) opt A method 200 for determining (A) is shown in FIG. 14. To facilitate description of certain aspects of the method 200, reference will be made to FIGS. 15-17. In the illustrated method 200, information from sources of initial data may be collected (block 204). The sources of initial data may include real-time data (e.g., data 180) and empirical historical data. In certain embodiments, the sources of initial data may include model data, ad-hoc assertions from an operator, or any other suitable source of information related to the EO production system. The empirical historical data may include data related to the EO production system generated over time during operation, and / or reference data obtained during off-line catalyst development and support or EO plant operation (e.g., reference data 62, 68 of FIGS. 7 and 8). The empirical historical data may include, among others, catalyst selectivity and temperature response to changes in moderator level, feed gas composition, pressure, EO production parameters, and gas hourly space velocity. The empirical historical data can also include information regarding the moderator level that maximizes catalyst selectivity, as well as the dependence of performance on moderator levels below and above the optimal level for a given set of operating conditions. The data processing system can evaluate the empirical historical data for variations to identify the best analytical parameters and allow tuning for a particular system, thereby improving the reliability and accuracy of the model estimates for maximum catalyst selectivity.

[0129] Method 200 also includes a decision step in query 208 to determine whether the data is stable. Prior to query 208, the data processing system may pre-process or clean (e.g., statistical cleaning) the data collected in block 204. Techniques such as, but not limited to, data smoothing, anomaly removal, and imputation may be used to clean the data. The inclusion of unstable or abnormal conditions other than the normal operation of the system with excessive deviations in catalyst selectivity and temperature may result in misinterpretation of trends. Thus, cleaning can remove outliers in the data. Furthermore, filtering data with large condition differences can also improve trend extraction and identification of optimal conditions for catalyst performance. For example, if the current real-time values ​​of certain operating conditions (e.g., EO production parameters, gas hourly space velocity, pressure, etc.) deviate significantly from normal operation, this may result in the selection of data that indicates potentially unstable operation or with variability in conditions that the model may not fully capture. To minimize these effects, the data processing system can apply EO production parameter-based filters, time-based filters, or any other suitable filters to select the most relevant data points.

[0130] Additionally, the data processing system may determine whether all required input data (e.g., EO production parameters, feed composition, GHSV, and pressure) are available. If all required input data is not available, the data processing system may apply imputation to estimate the missing input data using any suitable data processing technique. Thus, pre-processing the data may improve the overall accuracy and reliability of the optimal catalyst modifier levels determined using the systems and methods disclosed herein.

[0131] If, in query 208, the data processing system determines that the data is not stable (i.e., unstable), the data processing system provides a warning to wait for data stabilization (block 210). Thus, data may continue to be collected until the data is stable. Conversely, if the data is stable, the data processing system may calculate the model selectivity over time (S est ), model temperature (T est ), delta selectivity (ΔS), and corresponding delta temperature (ΔT) (block 212). For example, as described above, a data processing system (e.g., data processing system 182) stores data collected over time during operation of an EO generating system (e.g., EO generating system 100), reference data (e.g., the reference data of FIGS. 7-12), and models in a memory (e.g., memory 190). A memory (e.g., memory 186) stores instructions that, when executed by a processor (e.g., processor 184), retrieve the empirical historical data, real-time data, and reference data, and use the models and real-time data to generate one or more plots of catalyst selectivity and temperature over time.

[0132] 15 is a representative plot 216 of catalyst selectivity and temperature (a depiction of catalyst activity) as a function of days on stream that may be generated according to the behavior of block 212. Plot 216 includes measured catalyst selectivity data 218 and measured temperature data 220 collected by the EO production system over time. In addition, plot 216 includes model-estimated catalyst selectivity (S est ) data 224 and model estimated temperature (T est ) data 226. The plot 216 can be used to identify deviations of the measured data 218, 220 from the respective model-estimated data 224, 226. Since the model represents the moderator-optimized catalyst performance, any deviations of the measured data 218, 220 from the model-estimated data 224, 226, respectively, can be used to extract deviations of the moderator levels relative to the optimum. The methods described herein can be used to determine the correlation between the measured and model-estimated selectivities (Smeas and S est ), and between the measured temperature and the model-estimated temperature (T meas and T est ) is applied, systematic errors in the measurements and / or modeling are mitigated and do not affect its applicability. Thus, plot 216 can be used to identify trends in the data related to catalyst performance during operation of the system.

[0133] In certain embodiments, an operator of the EO generating system can select a section 230 along the plot 216 for further processing. The section 230 can include portions where the measured data 218, 220 deviate from the respective model estimated data 224, 226. The measured data 218, 220 and the model estimated data 224, 226 are combined to generate a selectivity (S) for the catalyst run-up up to the current real-time data point 232, in accordance with block 212 of FIG. meas and S est ) and temperature (T meas and T est ) as well as measured data and model estimates for the optimal moderator level (M). As shown in FIG. 15, the selected section 230 includes the most recent data collected by the EO generation system. The time frame used for section 230 includes real-time data points and may be a significant portion of the catalyst run, or the entire run. Typical time frames considered for section 230 may range from 1-2 days to 180 days, depending on the nature of the trends involved and which time frame provides the clearest trend. The selection of time frames to consider for the analysis may be much broader than certain existing optimization techniques, such as those described in WO 2016 / 108975 A1. The broader range of data available for use in the disclosed analysis may allow for a greater degree of optimization of the optimal moderator level (M) as compared to existing techniques. opt ) to increase and improve your ability to find

[0134] Returning to FIG. 14, the selectivity (S est ) and temperature (T estFollowing the model estimation of , the method 200 includes calculating a delta selectivity (ΔS) and a corresponding delta temperature (ΔT) for the recent data and the real-time data points (block 234). For example, the data processing system extracts measured data (e.g., measured data 218, 220) and model estimated data (e.g., model estimated data 224, 226) from a selected section (e.g., selected section 230) and determines ΔS and a corresponding ΔT between the respective measured and model estimated data. The data processing system calculates the measured selectivity data (S) for each time point according to Equation 8: meas ) (e.g., measurement data 218) and model-estimated selectivity data (S est ) (e.g., model estimated data 224) and for each time point, determine ΔS by taking the difference between the measured temperature data (T meas ) (e.g., measured data 220) and model-estimated temperature data (T est ) (eg, model estimation data 226) to determine ΔT.

[0135] The values ​​of ΔS and ΔT were further processed to determine the optimal regulator level (M opt ) may be estimated. Thus, the method 200 estimates RCl eff The operation of block 236 includes evaluating trends in the ΔS and ΔT values ​​to determine the RCl level (block 236). eff Step 236 thus includes a decision tree having various decision steps for determining ΔS. Step 236 is shown in more detail as decision tree 236 (FIG. 18). FIG. 16 shows a plot 238 of ΔS versus ΔT for selected data (e.g., the data in section 230 of FIG. 15) with a fitted curve through the data. opt ) is the point on the fitted curve that maximizes the optimal regulator level (M opt ) in ΔS opt The value of ΔT on the fitted curve of ΔS vs. ΔT corresponding to is ΔTopt.

[0136] FIG. 17 shows the fitted optimum value ΔS at point 294 from the respective ΔS and ΔT using Eqs. 10 and 11. opt and ΔT opt 17 shows a plot 240 that expresses the data of FIG. 16 in relative terms (e.g., relative selectivity difference (RSD), relative temperature difference (RTD)) by subtracting ΔS vs. ΔT. Plotting the data as RSD vs. RTD instead of ΔS vs. ΔT simply shifts the resulting curve so that its maximum point is fixed at (0,0) by definition. In the embodiment shown in FIG. 16, data set 242 represents ΔS and ΔT values ​​for individual days of data collected during a selected period (e.g., selected section 230). In the embodiment shown in FIG. 17, data set 246 represents RSD and RTD values ​​for individual days of data collected during a selected period. Other suitable time frequencies (e.g., hours instead of days) can also be used for plots 238, 240.

[0137] The models disclosed herein estimate the effect of operating conditions on the optimized regulator levels. Thus, the difference between the measured data and the model-estimated data at these operating conditions (i.e., ΔS and ΔT) effectively removes the effect of operating conditions on catalyst performance, leaving only the effect of regulator levels, as shown in Figures 16 and 17. As such, the trends shown in Figures 16 and 17 are similar to the trends of the fitted reference curves shown in Figure 10. Thus, the direction of catalyst adjustment (under-adjustment or over-adjustment) can be estimated by comparing Figure 16 with the reference curve of Figure 10. The RSD and RTD (e.g., Figure 17) can also be used to determine the magnitude of under- or over-adjustment. Similarly, the slope of the curves shown in Figures 16 or 17 can be calculated based on the slope of the curves for a given real-time data point, as described in more detail below and exemplified in Examples 1, 4, and 5.

[0138]

number

[0139] As mentioned above, the systems and methods disclosed herein also include providing alerts and / or actionable recommendations / guidance based on adjustment levels of the EO generating system relative to an optimum point determined using a combination of historical operating data, reference data, and model data. Thus, returning to FIG. 14, method 200 includes providing and displaying alerts and / or actionable recommendations or new regulator levels to the control system, per block 250. The data processing system may employ one or more decision trees (e.g., decision tree 236) having a set of outlined conditions that, when met, cause the data processing system to output the respective recommendation.

[0140] In embodiments where the catalyst is over-adjusted or under-adjusted, the data processing system can provide an audio or visual warning indicating that catalyst performance is not optimal. By way of non-limiting example, the warning may be an alarm, a notice on a display (e.g., display 192 or other remote display such as a phone, laptop, tablet, etc.), activation of a light, a color change in a light or display (e.g., from green to yellow or from green to red), or any other suitable audio or visual warning, and combinations thereof, that alerts an operator that the system is not operating at optimal conditions. In this way, an operator of an EO generating system can adjust the moderator level (moderator concentration, catalyst chloride effectiveness value (Cl eff ), or the regulator feed rate) needs to be adjusted.

[0141] In certain embodiments, the data processing system can provide the operator with actionable advice on recommended adjustments (i.e., target changes) to the regulator level to achieve maximum catalyst selectivity. As described above, the data processing system can use reference data (e.g., reference data 62, 68 in Figs. 7 and 8, respectively) obtained during offline testing of the catalyst or during initial EO plant operation, and ΔS and ΔT data obtained from real-time data, models, and historical data generated during operation of the EO production system to determine how much the catalyst is over- or under-adjusted. The recommendation is based on the results of the overall analysis of the ΔS and ΔT data. The data processing system can determine actionable recommendations and display the actionable recommendations on a display. For example, the data processing system can recommend increasing the regulator flow rate (e.g., feed rate) relative to the current flow rate if the catalyst is under-adjusted, or decreasing the regulator flow rate relative to the current flow rate if the catalyst is over-adjusted, to move the regulator level in a particular direction to reach an optimum value. In one embodiment, the data processing system may recommend increasing or decreasing the regulator level (M) by a particular amount relative to the current regulator level (e.g., +10%, +20%, -10%, -20%, etc.). In embodiments where the regulator level is optimal, the data processing system may provide a recommendation to maintain the current regulator flow rate and / or relative regulator level.

[0142] An operator of the EO production system may manually adjust the moderator level according to the recommendations provided to achieve maximum catalyst selectivity. In certain embodiments, the moderator level may be adjusted automatically. For example, a control system (e.g., control system 108 of FIG. 13) may output a signal to a metering device (e.g., flow control valve) that adjusts the amount of moderator 126 entering the reactor system (e.g., reactor system 102). As described above, the method 200 includes providing a new moderator level set point to the control system. In this embodiment, the control system (e.g., control system 108) adjusts the flow rate (e.g., feed rate) of the moderator to decrease or increase the amount of moderator in the feed gas depending on the catalyst adjustment level determined by the disclosed method and analysis. The system may include an override or bypass feature that allows an operator to override / bypass the recommendation.

[0143] The method 200 disclosed herein can be iterative. As operational data of the EO generation system continues to be collected and stored in the data processing system, the amount of historical data of the system increases and can be used to fine-tune the model and improve the accuracy of the estimated optimum point for maximum catalyst selectivity. The data processing system can continuously evaluate the historical data to determine the best analytical parameters that allow fine-tuning of the system to provide accurate and reliable trends. The behavior of the method 200 may be repeated continuously in real time while the EO generation system is in operation or on demand (e.g., when initiated by an operator or triggered by a change in an EO generation parameter, for example).

[0144] As discussed above, the data processing system uses one or more decision trees to process and interpret the data (e.g., data points 242 and 246) to evaluate the catalyst adjustment status and provide warnings / recommendations and / or new adjustment level set points. eff

[0145]

number

[0146] For example, to identify a trend, the data processing system may fit the data points 242 to a polynomial with a downward concavity or any other suitable non-linear representation. A variety of techniques may be used to fit the curve 286, including, but not limited to, least squares regression, non-linear regression, robust regression, weighted regression, and constrained optimization, among others. If no trend is identified, the data processing system provides a warning to proceed to an exploratory modifier step (block 287). Example 3 below provides an illustration of the results of this decision tree.

[0147] In one particular embodiment, the data processing system may determine a confidence level in the fitted curve 286 before proceeding to a subsequent step in the decision tree 236. That is, the data processing system may check whether the curve 286 is a good fit to the data points 242. For example, the data processing system may determine a minimum acceptable R 2 or adjusted R 2 A defined adjustable threshold, such as a metric, may be used to evaluate the goodness of fit of the curve 286. In one embodiment, the data processing system may check whether the real-time data points 288 match the fitted curve 286. However, any other suitable technique may be used to ensure the reliability (i.e., goodness) of the fitted curve 286 and the optimum point 294.

[0148] The fitted curve 286 includes a maximum or optimum point 294. The optimum point 294 is the maximum or optimum point of the measured catalyst selectivity (S meas ) and model selectivity (S est ) is defined as the point where the difference ΔS between the optimal point (ΔT opt ,ΔS opt ) In certain embodiments, the data processing system can also determine whether there is sufficient ΔS and ΔT data to accurately determine the optimum point 294. As will be appreciated, using the systems and methods disclosed herein, the model estimates (ΔS opt Determining the maximum catalyst selectivity for ΔS (e.g., the value of ΔS at the optimum point 294) does not depend on knowing the precise or exact moderator concentration, as illustrated by the absence of the moderator concentration in the plot 238 illustrated in FIG. 16. That is, unlike certain existing techniques, the techniques disclosed herein do not rely on knowing the maximum catalyst selectivity (S opt Optimal regulator level (M opt 16, the selected data points 242 encompass both sides 290, 292 of the optimal point 294, indicating that the data (e.g., data 218, 220) in the selected time period (e.g., section 230) represent both under-regulation and over-regulation states.

[0149] Returning to FIG. 18, following the identification of a trend according to query 284, decision tree 236 includes, in query 298, determining whether a current real-time data point (e.g., real-time data point 288) is within a prediction boundary line. For example, the real-time data point is compared to a fitted curve to determine whether it is within a prediction boundary line. The prediction boundary line can be defined as a fixed range vertically away from the fitted curve (i.e., parallel to the y-axis) known to represent a typical spread in the data, or by statistical means using a standard deviation of the measurements or a standard error of the fit. The prediction boundary line may be within about ±0.1 selectivity % to about ±0.5 selectivity %, e.g., within a range of −0.5 to +0.5, or within a range of −0.1% to +0.1%. For example, as shown in FIG. 16, real-time data point 288 is essentially along fitted curve 286 and is therefore within a prediction boundary line. A situation in which a real-time data point is outside a prediction boundary line is shown later in Example 2 (see FIG. 20).

[0150] In the decision tree 236 of FIG. 18, if the data processing system determines that the real-time data point is outside the prediction boundary, the data processing system provides a warning indicating that the real-time data point is outside the prediction boundary and to wait for the data to stabilize (block 289). However, if the data processing system determines that the real-time data point is within the prediction boundary, the data processing system proceeds to determine the slope of the fitted curve. Thus, the decision tree 236 includes determining the slope of the fitted curve (e.g., the slope of the ΔS vs. ΔT curve 286) for the real-time data point (block 300). For example, the fitted curve may be used to determine the slope of the fitted curve.

[0151]

number

[0152] Conversely, data points 242 along the positive slope of curve 286 (i.e., to the left of optimal point 294) indicate that the catalyst selectivity is not optimal and is over-regulated; that is, the regulator level is higher than the optimal regulator level for these points. In the embodiment shown in Figure 16, the slope 302 of the fitted curve 286 at the real-time data points 288 is approximately +0.28% / °C, which is meant to be to the left of optimal point 294 and represents an over-regulated catalyst.

[0153] Following the determination of the slope, the decision tree 236 of FIG. 18 includes, in a query 304, determining whether the real-time value of the slope is within the normal reference boundaries. For example, the data processing system compares the real-time slope value to the normal reference boundaries shown in FIG. 11. As a non-limiting example, the normal reference boundaries are within a range of about ±1% / °C to about ±3% / °C. As defined in the plot 82 of FIG. 11, the normal reference boundaries for the slope value are ±2% / °C. In the embodiment shown in FIG. 16, the slope 302 of the fitted curve 286 at the current real-time data point 288 is about +0.28% / °C, which is within the normal reference boundaries of ±2% / °C. If the data processing system determines that the slope value is not within the normal reference boundaries, the data processing system provides a warning that the catalyst is in an over-adjusted state and to manually reduce the regulator level (block 306).

[0154] However, if the data processing system determines that the slope is within the normal reference boundaries (as shown in FIG. 16), the data processing system calculates the slope at the real-time point from the slope at the real-time point and the reference curve.

[0155]

number

[0156] Real-time RCl eff

[0157]

number

[0158] As shown in the embodiment illustrated in FIG. 17, centering the maximum 294 at coordinates (0,0) shifts the RTD and RSD coordinates of the real-time data point 288 from (0.01° C., 0.13%) (see FIG. 16) to (−2.47° C., −0.4%). These coordinate values ​​are compared to the respective reference curves 60, 64 shown in FIG. 9. For example, a comparison may be made first using the RTD value (e.g., −2.47° C.), which determines the side of the curve 60 that will be used to estimate the adjustment level. In FIG. 9, the RTD value corresponds to point 318 (−2.47° C.) on the reference curve 64. Point 318 is located to the right of the maximum 72 (e.g., RCl eff >0), indicating hyperregulation. The corresponding RCl at point 318 eff is estimated to be overregulated by +0.117, or 11.7%.

[0159] Using the knowledge that the real-time data point (e.g., real-time data point 288) is overadjusted, a -0.4% RSD value (real-time data point 288 in FIG. 17) is plotted in plot 70 on the right portion of reference curve 60 at point 320 (i.e., the portion to the right of maximum value 72). Point 320 corresponds to an RCl of 0.112. eff , or corresponds to 11.2% over-adjustment. If the RTD value is determined to be to the left of the maximum value 72 (under-adjustment), then the left side of the reference curve 60 is selected for plotting the RSD value.

[0160] Therefore, in the examples shown in Figs. 16 and 17, real-time RCl eff

[0161]

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[0162]

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[0163]

number

[0164]

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[0165]

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[0166] In one embodiment, real-time RCl according to the behavior of real-time block 310 of FIG. eff

[0167]

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[0168]

number

[0169]

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[0170]

number

[0171] The decision tree 236 also determines the real-time RCl in the query 316. eff Value of

[0172]

number

[0173]

number

[0174] However, real-time RCl eff Value of

[0175]

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[0176]

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[0177] For example, referring to the embodiment shown in FIG. 16 and FIG. 17, a real-time RCl eff The average value of

[0178]

number

[0179]

number

[0180]

number

[0181]

number

[0182] This change represents a 9.3% decrease in regulator levels.

[0183] Similarly, in the present example, if the make-up modifier feed rate is used as the modifier level to optimize the catalyst and its real-time value is 2.56 kg / h, then Equation 4 can be rewritten as: eff can be applied together to calculate the target optimal level:

[0184]

number

[0185] This change also corresponds to a 9.3% decrease in the moderator level. This method of identifying the optimum moderator level target via the make-up moderator feed rate can be used when gas phase chloride concentrations are not easily or accurately measurable, as is sometimes the case in actual plant operations.

[0186] Real-time RCl effIf the value of is negative, indicating an underadjusted condition, the data processing system provides a warning that the catalyst is underadjusted and recommends increasing the regulator level to a specified target or by a specified amount (block 328). The warning and / or recommendation output by the data processing system may be displayed on a display (e.g., display 192) of the EO generation system (e.g., system 100) along with the amount by which the regulator level should be decreased / increased.

[0187] It should be noted that the method disclosed herein does not require precise or accurate measurement or knowledge of the gas phase moderator concentration, but rather uses performance trend and model analysis to determine the direction and magnitude of the required change in moderator level. As mentioned above, techniques that rely on measuring the moderator concentration in the feed gas can be unreliable because it is difficult to measure accurately and does not necessarily reflect the moderator concentration on the surface of the catalyst. The present invention overcomes these limitations by evaluating the effect of the moderator level on catalyst performance changes. Furthermore, by plotting ΔS as a function of ΔT or RSD as a function of RTD, trends in a single trend versus two trends can be seen, as shown in Figure 15, thereby improving the usability of the system.

[0188] Provided below are additional examples illustrating other potential scenarios and warnings according to the decision tree 236 of Figure 18. To facilitate explanation of the following examples, reference is made to Figures 19-23. EXAMPLES

[0189] Example 1 - Data to one side of the optimum, where the real-time points are under-adjusted FIG. 19 shows a plot 336 of ΔS versus ΔT for a data set 338 over a selected time range. The data set 338 includes real-time data points 342. In the illustrated example, an acceptable fitted curve is generated and a trend 346 is observed. The real-time data points 342 are evaluated to determine whether they are within the prediction boundary line. As shown in FIG. 19, the real-time data points 342 are close to the fitted curve 346 and are within the prediction boundary line (e.g., about ±0.1% to about ±0.5%, e.g., the prediction boundary line may be within the range of -0.5 to +0.5, or within the range of -0.1% to +0.1%). Thus, the data processing system determines the slope of the fitted curve 346. For example, using the fitted curve 346, the slope 350 of the curve 346 at the real-time data points 342 is determined as -0.36% / °C. This slope value is compared to the normal reference boundary (see FIG. 11) which is ±2% / °C. The calculated slope value of curve 346 is within this range. Therefore, the data processing system calculates the real-time RCl eff Proceed to determine.

[0190] In the illustrated example, the data sets 338 within the selected time range fall only along the negative slope region of the fitted curve 346, indicating that the data falls only on the under-adjusted side of the optimum.

[0191] Additionally, the fitted curve 346 does not have a maximum value within the fitted data range. Therefore, only the slope 350 value can be calculated using the reference curve 76 of FIG. eff For example, referring to FIG. 12, a calculated slope of −0.36% / ° C. corresponds to point 354 on curve 76. Curve 76 is used to estimate the estimated real-time RCl. eff is determined to be −0.198, or 19.8% underregulated. The percentage change required to move the regulator level to the optimal target level is calculated using Equation 5.

[0192] Therefore, the regulator level is (1 / (1-0.198)-1) *100% or 24.7% to bring the system back to the optimal regulator level (eg, block 314 of FIG. 18).

[0193] Real-time RCl eff The value of RCl is also evaluated to determine whether it is close to 0. For example, using a typical range of about ±0.02 to about ±0.04, the estimated RCl for real-time point 342 can be calculated as described above based on knowledge of the ability to measure and control regulator levels. eff is not close to 0. Therefore, the estimated RCl at real-time point 342 eff The value is the estimated real-time RCl eff is further evaluated to determine whether the real-time RCl eff The value of is negative (-0.198). Therefore, the data processing system outputs an underadjustment warning 328 and recommends increasing the reference level by 24.7%.

[0194] Example 2 - Real-time points are outside the prediction perimeter 20 shows a plot 360 of ΔS and ΔT for a data set 362 having real-time data points 364 for a selected time range. In the illustrated example, an acceptable fit of the data set 362 is produced and a trend 368 (i.e., a fitted curve) is seen.

[0195] The real-time data point 364 is evaluated to determine whether it falls within the predicted boundary line defined by the upper curve 370 and the lower curve 372, respectively. As described above, the predicted boundary line width is about ±0.1% to about ±0.5%, for example, the boundary line width may be in the range of -0.5 to +0.5 or -0.1% to +0.1% relative to the fitted curve 368, based on knowledge of typical variability in the data. In this particular example, the current real-time data point 364 is not between the predicted boundary line curves 370 and 372, indicating that the current real-time data point 364 does not follow the same trend 368 as the other data in the data set 362 that is between the predicted boundary line curves 370 and 372. This may indicate that the system has not yet stabilized. Thus, the data processing system outputs a warning 289 indicating that the real-time data point 364 is outside the predicted boundary line, and the operator should wait for stabilization until a clearer trend is obtained.

[0196] Example 3 - No trend detected 21 shows a plot 400 of ΔS and ΔT for a data set 404 having real-time data points 402. As shown in plot 400, there is no acceptable trend for the selected data window that meets the goodness-of-fit or range criteria. Therefore, in this particular example, the data processing system outputs a warning 287 to proceed with a step change in the exploratory modulator.

[0197] The size and direction of such a search step may be determined by an operator or a control system (e.g., control system 108) based on the control and measurement capabilities of the EO plant (e.g., about a 3-5% change is typical). The search step allows for expanding the available data, increasing the likelihood of later obtaining trends that will allow for better evaluation of catalyst adjustments.

[0198] Example 4 - Severe Overregulation FIG. 22 shows a plot 410 of ΔS and ΔT for a data set 412 with real-time data points 416. In the illustrated example, an acceptable fit is produced and a fitted curve 418 is determined. The real-time data points 416 are close to the fitted curve (e.g., within a specified range above / below the fitted curve 418, e.g., within ±0.3%) and are within the prediction boundary. Thus, a local slope 420 of the fitted curve 418 is determined and evaluated at the ΔT of the current real-time data point 416 using graphical analysis or by taking the derivative of the fitted curve 418 as described above. The determined value of the slope 420 is +2.3% / °C. This value is compared to the normal reference boundary given in FIG. 11, which is ±2% / °C. Comparing the value of the slope 420 to the normal reference boundary indicates that the value is outside this range. Thus, the plant operation is outside of the targeted normal range and the data processing system outputs a warning 306 that the catalyst is likely severely over-adjusted (e.g., over-adjusted by more than 30%) and provides a recommendation to manually reduce the regulator level. The operator may adjust the regulator level as recommended and wait for the system to stabilize and trend back into a more defined normal range, after which a specific adjustment level target can be determined.

[0199] Example 5 - Real-time data points close to optimal value FIG. 23 shows a plot 426 with ΔS and ΔT for a data set 428 with real-time data points 430. In the illustrated example, an acceptable fit is produced and a trend 432 is seen. The real-time data points 430 are close to the trend 432 (i.e., the fitted curve) and are within the expected boundaries (e.g., within about ±0.1% to about ±0.5%, e.g., within the range of -0.5 to +0.5, or within the range of -0.1% to +0.1%). Using the fitted curve 432, a slope 440 of the curve 432 at the real-time data points 430 is determined as +0.02% / °C. The value of slope 440 is compared to the normal reference boundaries (e.g., -2% / °C to +2% / °C) shown in FIG. 11. As shown, the calculated slope 440 of the curve 432 is within the normal boundaries.

[0200] Therefore, the RCl at the real time point eff The value of R is estimated using the slope 440 and compared to the reference curve 76 of FIG. 12. The value of the estimated slope 440 is +0.02% / C, which corresponds to point 442 on the reference curve 76 of FIG. 12. Using the reference curve 76, the estimated R for the real time point is eff is determined to be +0.011, or 1.1% overadjusted, which is within the normal range of ±0.02 to ±0.04, which is the RCl eff is considered to be within the measurement and control noise of 0.

[0201] Therefore, the data processing system outputs a warning 318 indicating that the catalyst is close to the optimum and recommends maintaining the regulator level.

[0202] As mentioned above, the techniques disclosed herein can be used to determine optimal modifier levels to achieve maximum catalyst selectivity in a reliable and robust manner. The systems and methods use a combination of reference data, historical data, and model data to determine optimal modifier levels and catalyst performance without relying on precise and accurate monitoring of modifier concentrations. The disclosed systems and methods can provide real-time warnings / recommendations on how to adjust modifier levels to reach maximum catalyst selectivity. By using historical data specific to the EO generating system and catalyst-specific data, the model can be fine-tuned to provide accurate and reliable estimates of optimal modifier levels and maximum catalyst selectivity. In this way, the modifier levels can be changed stepwise to determine the optimum value, mitigating the shortcomings associated with existing techniques that rely on monitoring gas-phase modifier concentrations, which may not indicate the amount of modifier on the catalyst surface. Thus, by using the disclosed systems and methods, the accuracy and reliability of optimal modifier levels to achieve maximum catalyst selectivity can be improved compared to existing techniques that rely on modifier concentrations to determine maximum catalyst selectivity.

[0203] Additionally, the disclosed systems and methods provide actionable guidance / recommendations as to both the direction and magnitude of adjustment needed to achieve maximum catalyst selectivity, or whether more data is needed to obtain a usable trend.

[0204] The present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics, and the described embodiments are to be considered in all respects only as illustrative and not restrictive. Specific embodiments of the present disclosure will be described below.

[0205] [Embodiment 1] 1. A method for maximizing the selectivity (S) of an epoxidation catalyst in an ethylene oxide reactor system, comprising: A measured reactor selectivity (S) from an ethylene oxide production system configured to convert a feed gas containing ethylene and oxygen to ethylene oxide in the ethylene oxide reactor system in the presence of the epoxidation catalyst and a chloride-containing catalyst modifier. meas ), the measured reactor temperature (T meas ), and one or more operational parameters, wherein the epoxidation catalyst comprises silver and a promoting amount of rhenium (Re), and the measured reactor selectivity (S meas ), the measured reactor temperature (T meas ), and the one or more operational parameters include real-time and historical operational data points over time generated by the ethylene oxide production system; (a) The model is used to determine the optimal regulator level (M opt The model-estimated selectivity (S est ) and model-estimated temperature (T est ) wherein said model estimated selectivity (S est ) and the model estimated temperature (T est ), determined based on at least one operational parameter of the one or more operational parameters at said time, wherein the at least one operational parameter does not include a chloride-containing modifier level, and wherein the model is based at least in part on empirical historical data associated with the epoxidation catalyst, the ethylene oxide production system, or both; (b) for each of said time points, said measured reactor selectivity (S meas ) and the model-estimated selectivity (S est ) and the measured reactor temperature (T meas ) and the model estimated temperature (T est ) determining the difference (ΔT) between (c) fitting a curve to the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve; (d) The approximate curve and ΔS(ΔS real-time ) and ΔT(ΔT real-time Real-time relative effective regulator levels based on real-time values ​​of

[0206]

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[0207]

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[0208]

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[0209]

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[0210]

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[0211]

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[0212]

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[0213] [Embodiment 2] ΔS(ΔS real-time ) and ΔT(ΔT real-time 2. The method of claim 1, wherein the slope of the fitted curve at the real-time value of (a) is within normal reference boundaries, the normal reference boundaries being within a range of ±1% / °C to ±3% / °C.

[0214] [Embodiment 3] Real-time RCl eff

[0215]

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[0216]

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[0217]

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[0218] [Embodiment 4] ΔS(ΔS real-time 4. The method according to any one of the preceding claims, wherein the real-time value of is within a prediction boundary of the approximation curve, the prediction boundary being within a range of ±0.1% to ±0.5%.

[0219] [Embodiment 5] The RCl eff But the real-time value

[0220]

number

[0221] [Embodiment 6] The real-time RCl in the ethylene oxide reactor system eff

[0222]

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[0223] [Embodiment 7] 7. The method of any of the preceding claims, wherein the one or more operational parameters comprise gas hourly space velocity (GHSV), pressure, the moderator level, feed gas composition, EO production parameters, and combinations thereof, wherein the EO production parameters are selected from the group comprising product gas ethylene oxide concentration, the change in moles of EO produced from the inlet to the outlet of a reactor in the ethylene oxide reactor system, ethylene oxide production rate, ethylene oxide production rate per mass of silver loaded in the reactor, ethylene oxide production rate per mass of catalyst, and work rate.

[0224] [Embodiment 8] One or more tangible, non-transitory, machine-readable media configured to maximize the selectivity (S) of an epoxidation catalyst in an ethylene oxide reactor system; and (a) using a model to determine, for real time and historical points over time, an optimal moderator level (M opt The model-estimated selectivity (S est ) and model-estimated temperature (T est ), wherein the model is based at least in part on empirical historical data relating to the epoxidation catalyst, the ethylene oxide production system, or both, and the at least one operational parameter does not include chloride-containing modifier levels, and the epoxidation catalyst includes silver and a promoting amount of rhenium (Re); (b) For each of the time points, the measured reactor selectivity (S meas ) and the model-estimated selectivity (S est ) and the measured reactor temperature (T meas ) and the model estimated temperature (T est ) and determining the difference (ΔT) between the measured reactor selectivity (S meas ), the measured reactor temperature (T meas ) comprises real time and historical operational data points generated by the ethylene oxide production system at said time; (c) fitting a curve to the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve; (d) The approximate curve and ΔS(ΔS real-time ) and ΔT(ΔT real-time Real-time relative effective regulator levels based on real-time values ​​of

[0225]

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[0226]

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[0230]

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[0232] [Embodiment 9] Real-time RCl eff

[0233]

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[0234]

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[0235]

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[0236] [Embodiment 10] The RCl eff The real-time level (M) of the regulator level (M) is changed from its real-time value to the optimal level of 0.0, or equivalent. real-time ) to its optimal value (M opt Change in target to (M real-time ), an absolute target optimal regulator level (Mopt 10. One or more machine-readable media as described in embodiment 8 or 9, comprising instructions for making a change to

[0237] [Embodiment 11] The real-time RCl in the ethylene oxide reactor system eff

[0238]

number

[0239] [Embodiment 12] 1. A system comprising: a reactor disposed within an ethylene oxide production system, the reactor comprising ethylene, oxygen, an epoxidation catalyst, and a chloride-containing catalyst modifier, the reactor configured to convert the ethylene and the oxygen to ethylene oxide, the epoxidation catalyst comprising silver and a promoting amount of rhenium (Re); A display and The ethylene oxide production system was then subjected to a measurement of reactor selectivity (S meas ), the measured reactor temperature (T meas ), and one or more operational parameters, meas ), the measured reactor temperature (T meas ), and the one or more operational parameters include real time and historical operational data points generated by the ethylene oxide production system over time, the data processing system comprising a processor and, when executed by the processor, (a) The model is used to determine the optimal regulator level (M opt The model-estimated selectivity (S est ) and model-estimated temperature (T est ) wherein said model estimated selectivity (S est ) and temperature (T est ), determined based on at least one operational parameter of the one or more operational parameters at said time, wherein the at least one operational parameter does not include a chloride-containing modifier level, and wherein the model is based at least in part on empirical historical data associated with the epoxidation catalyst, the ethylene oxide production system, or both; (b) for each of said time points, said measured reactor selectivity (S meas ) and the model-estimated selectivity (S est ) and the measured reactor temperature (T meas ) and the model estimated temperature (T est ) determining the difference (ΔT) between (c) fitting a curve to the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve; (d) The approximate curve and ΔS(ΔS real-time ) and ΔT(ΔT real-time Real-time relative effective regulator levels based on real-time values ​​of

[0240]

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[0241]

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[0242]

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[0243]

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[0244]

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[0245]

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[0246]

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[0247] [Embodiment 13] Real-time RCl eff

[0248]

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[0249]

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[0250]

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[0251] [Embodiment 14] The data processing system is adapted to process the real-time RCl eff

[0252]

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Claims

1. 1. A method for maximizing the selectivity (S) of an epoxidation catalyst in an ethylene oxide reactor system, comprising: A measured reactor selectivity (S) from an ethylene oxide production system configured to convert a feed gas containing ethylene and oxygen to ethylene oxide in the ethylene oxide reactor system in the presence of the epoxidation catalyst and a chloride-containing catalyst modifier. ｍｅａｓ ), the measured reactor temperature (T ｍｅａｓ ), and one or more operational parameters, wherein the epoxidation catalyst comprises silver and a promoting amount of rhenium (Re), and the measured reactor selectivity (S ｍｅａｓ ), the measured reactor temperature (T ｍｅａｓ ), and the one or more operational parameters include real-time and historical operational data points over time generated by the ethylene oxide production system; (a) The model was used to determine the optimal regulator level (M ｏｐｔ The model-estimated selectivity (S ｅｓｔ ) and model estimated temperature (T ｅｓｔ ) where the model estimated selectivity (S ｅｓｔ ) and the model estimated temperature (T ｅｓｔ ), wherein the model is determined based on at least one operational parameter of the one or more operational parameters at said time, the at least one operational parameter not including a chloride-containing modifier level, and the model is based at least in part on empirical historical data associated with the epoxidation catalyst, the ethylene oxide production system, or both; (b) for each of said time points, said measured reactor selectivity (S ｍｅａｓ ) and the model-estimated selectivity (S ｅｓｔ ) and the measured reactor temperature (T ｍｅａｓ ) and the model estimated temperature (T ｅｓｔ ) determining the difference (ΔT) between (c) fitting a curve to the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve; (d) The approximate curve and ΔS (ΔS ｒｅａｌ－ｔｉｍｅ ) and ΔT (ΔT ｒｅａｌ－ｔｉｍｅ Real-time relative effective regulator levels based on real-time values ​​of [0010] and determining (e) the real-time RCl ｅｆｆ [0025] and outputting actionable recommendations based on the RCl. ｅｆｆ is the real-time value [0030] to an optimal level of 0.0 by definition or an equivalent absolute regulator level target (M ｏｐｔ ) so that the regulator level (M) is changed to its optimal value (M ｏｐｔ Change in target to (M ｃｈａｎｇｅ ) and outputting the (f) displaying the actionable recommendations on a display; and using a processor to perform the steps of: ｅｆｆ is the optimal regulator level (M ｏｐｔ (M) minus 1; RCl ｅｆｆ =(M / M ｏｐｔ )-1 The moderator level (M) may be adjusted to control the total or weighted total concentration of chloride species in the feed gas to the ethylene oxide reactor system, the chloride make-up feed rate, or the catalyst chloride effectiveness value (Cl ｅｆｆ ), which is [0045] is calculated as whereby [MC], [EC], [EDC], and [VC] are the concentrations in ppmv of methyl chloride (MC), ethyl chloride (EC), ethylene dichloride (EDC), and vinyl chloride (VC), respectively, and [CH ４ ], [C ２ H ６ ] and [C ２ H ４ ] are the concentrations in mole percent of methane, ethane, and ethylene in the feed gas, respectively, and the regulator level (M) is its real-time level (M ｒｅａｌ－ｔｉｍｅ ) to its optimal level (M ｏｐｔ ) and ｅｆｆ The recommended change to bring the value of β-K to its optimal level of 0.0, expressed as a percentage, is: [0050] or as the equivalent incremental change in regulator level, or equivalently, and the absolute recommended optimal regulator level goal (M ｏｐｔ )but, [006] is defined as The real-time RCl ｅｆｆ [0070] but, (i) (ΔS ｒｅａｌ－ｔｉｍｅ ) and ΔT (ΔT ｒｅａｌ－ｔｉｍｅ ) and comparing said slope to a reference curve for the epoxidation catalyst; or (ii) From the approximation curve, the maximum ΔS (ΔS ｏｐｔ ) and the corresponding ΔT at the maximum ΔS (ΔT ｏｐｔ ) wherein said ΔS ｏｐｔ is the optimal RCl ｅｆｆ to arise in, to determine, From the ΔS to the ΔS ｏｐｔ and the relative selectivity difference (RSD) is calculated by subtracting the ΔT from the ΔT ｏｐｔ Calculating the relative temperature difference (RTD) by subtracting the RSD (RSD ｒｅａｌ－ｔｉｍｅ ) and the RTD (RTD ｒｅａｌ－ｔｉｍｅ ) to a reference curve for the epoxidation catalyst, or by a combination of methods (i) and (ii); The reference curves are generated from previous laboratory tests, pilot plant tests, or early plant runs, and the selectivity deviation and temperature deviation versus optimum are plotted against the relative effective moderator level (RCI ｅｆｆ ) or by relating the slope of the plot of the selectivity deviation plotted against the temperature deviation to the relative effective moderator level (RCl ｅｆｆ ) a method of relating

2. ΔS (ΔS ｒｅａｌ－ｔｉｍｅ ) and ΔT (ΔT ｒｅａｌ－ｔｉｍｅ 2. The method of claim 1, wherein the slope of the fitted curve at the real-time value of (a) is within normal reference boundaries, the normal reference boundaries being within a range of ±1% / °C to ±3% / °C.

3. The real-time RCl ｅｆｆ [0080] When is 0 or close to 0, the optimal RCl ｅｆｆ and 0 or its vicinity is within the range of ±0.01 to ±0.05, and the epoxidation catalyst is [0090] is positive and not at or near zero, and the epoxidation catalyst is over-regulated when [0010] 3. The method of claim 1 or 2, wherein the signal is under-adjusted when is negative and not at or near zero.

4. One or more tangible, non-transitory, machine-readable media configured to maximize the selectivity (S) of an epoxidation catalyst in an ethylene oxide reactor system; (a) using a model to determine, for real time and historical points over time, an optimal moderator level (M ｏｐｔ The model-estimated selectivity (S ｅｓｔ ) and model estimated temperature (T ｅｓｔ ), wherein the model is based at least in part on empirical historical data relating to the epoxidation catalyst, the ethylene oxide production system, or both, and the at least one operational parameter does not include chloride-containing modifier levels, and the epoxidation catalyst includes silver and a promoting amount of rhenium (Re); (b) For each of said time points, the measured reactor selectivity (S ｍｅａｓ ) and the model-estimated selectivity (S ｅｓｔ ) and the measured reactor temperature (T ｍｅａｓ ) and the model estimated temperature (T ｅｓｔ ) and determining the difference (ΔT) between the measured reactor selectivity (S ｍｅａｓ ), the measured reactor temperature (T ｍｅａｓ ) includes real time and historical operational data points generated by the ethylene oxide production system at said time; (c) fitting a curve to the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve; (d) The approximation curve and ΔS (ΔS ｒｅａｌ－ｔｉｍｅ ) and ΔT (ΔT ｒｅａｌ－ｔｉｍｅ Real-time relative effective regulator levels based on real-time values ​​of ##EQU00011## and determining (e) the real-time RCl ｅｆｆ ##EQU00012## and outputting actionable recommendations based on the RCl. ｅｆｆ is the real-time value ##EQU00013## to an optimal level of 0.0 by definition or an equivalent absolute regulator level target (M ｏｐｔ ) so that the regulator level (M) is changed to its optimal value (M ｏｐｔ Change in target to (M ｃｈａｎｇｅ ) and outputting the (f) displaying the actionable recommendation on a display; The RCl ｅｆｆ is the optimal regulator level (M ｏｐｔ (M) minus 1; RCl ｅｆｆ =(M / M ｏｐｔ )-1 The moderator level (M) may be adjusted to control the total or weighted total concentration of chloride species in the feed gas to the ethylene oxide reactor system, the chloride make-up feed rate, or the catalyst chloride effectiveness value (Cl ｅｆｆ ), which is ##EQU14## is calculated as whereby [MC], [EC], [EDC], and [VC] are the concentrations in ppmv of methyl chloride (MC), ethyl chloride (EC), ethylene dichloride (EDC), and vinyl chloride (VC), respectively, and [CH ４ ], [C ２ H ６ ] and [C ２ H ４ ] are the concentrations in mole percent of methane, ethane, and ethylene in the feed gas, respectively, and the regulator level (M) is its real-time level (M ｒｅａｌ－ｔｉｍｅ ) to its optimal level (M ｏｐｔ ) and ｅｆｆ The recommended change to bring the value of β-K to its optimal level of 0.0, expressed as a percentage, is: ##EQU00015## or as the equivalent incremental change in regulator level, or equivalently, and the absolute recommended optimal regulator level goal (M ｏｐｔ )but, ##EQU00016## is defined as The real-time RCl ｅｆｆ ##EQU00017## but, (i) ΔS (ΔS ｒｅａｌ－ｔｉｍｅ ) and ΔT (ΔT ｒｅａｌ－ｔｉｍｅ ) and comparing said slope to a reference curve for the epoxidation catalyst; or (ii) From the approximation curve, the maximum ΔS (ΔS ｏｐｔ ) and the corresponding ΔT at the maximum ΔS (ΔT ｏｐｔ ), wherein the maximum ΔS (ΔS ｏｐｔ ) is the optimal RCl ｅｆｆ determining the ΔS ｒｅａｌ－ｔｉｍｅ From the above ΔS ｏｐｔ The real-time relative selectivity difference (RSD) is calculated by subtracting ｒｅａｌ－ｔｉｍｅ ) and the ΔT ｒｅａｌ－ｔｉｍｅ From the above ΔT ｏｐｔ By subtracting ｒｅａｌ－ｔｉｍｅ ) and calculating the RSD (RSD ｒｅａｌ－ｔｉｍｅ ) and the RTD (RTD ｒｅａｌ－ｔｉｍｅ ) relative to a reference curve for the epoxidation catalyst; or a combination of methods (i) and (ii), wherein the reference curve is generated from previous laboratory tests, pilot plant tests, or earlier plant runs, and the selectivity deviation and temperature deviation versus optimum are correlated with the relative effective moderator level (RCl ｅｆｆ ) or by relating the slope of the plot of the selectivity deviation plotted against the temperature deviation to the relative effective moderator level (RCl ｅｆｆ ) one or more tangible, non-transitory, machine-readable media.

5. 1. A system comprising: a reactor disposed within an ethylene oxide production system, the reactor comprising ethylene, oxygen, an epoxidation catalyst, and a chloride-containing catalyst modifier, the reactor configured to convert the ethylene and the oxygen to ethylene oxide, the epoxidation catalyst comprising silver and a promoting amount of rhenium (Re); A display and The ethylene oxide production system is then subjected to a measurement of reactor selectivity (S ｍｅａｓ ), the measured reactor temperature (T ｍｅａｓ ), and one or more operational parameters, ｍｅａｓ ), the measured reactor temperature (T ｍｅａｓ ), and the one or more operational parameters include real time and historical operational data points generated by the ethylene oxide production system over time, the data processing system comprising a processor and, when executed by the processor, (a) The model was used to determine the optimal regulator level (M ｏｐｔ The model-estimated selectivity (S ｅｓｔ ) and model estimated temperature (T ｅｓｔ ) where the model estimated selectivity (S ｅｓｔ ) and temperature (T ｅｓｔ ), wherein the model is determined based on at least one operational parameter of the one or more operational parameters at said time, the at least one operational parameter not including a chloride-containing modifier level, and the model is based at least in part on empirical historical data associated with the epoxidation catalyst, the ethylene oxide production system, or both; (b) for each of said time points, said measured reactor selectivity (S ｍｅａｓ ) and the model-estimated selectivity (S ｅｓｔ ) and the measured reactor temperature (T ｍｅａｓ ) and the model estimated temperature (T ｅｓｔ ) determining the difference (ΔT) between (c) fitting a curve to the delta selectivity (ΔS) data points as a function of the corresponding delta temperature (ΔT) data points to obtain a fitted curve; (d) The approximation curve and ΔS (ΔS ｒｅａｌ－ｔｉｍｅ ) and ΔT (ΔT ｒｅａｌ－ｔｉｍｅ Real-time relative effective regulator levels based on real-time values ​​of [0018] and determining (e) the real-time RCl ｅｆｆ [0019] and outputting actionable recommendations based on the RCl. ｅｆｆ is the real-time value [0020] to an optimal level of 0.0 by definition or an equivalent absolute regulator level target (M ｏｐｔ ) so that the regulator level (M) is changed to its optimal value (M ｏｐｔ Change in target to (M ｃｈａｎｇｅ ) and outputting the (f) displaying the actionable recommendations on a display; and one or more tangible, non-transitory, machine-readable media comprising instructions configured to: ｅｆｆ is the optimal regulator level (M ｏｐｔ (M) minus 1; RCl ｅｆｆ =(M / M ｏｐｔ )-1 The moderator level (M) may be adjusted to control the total or weighted total concentration of chloride species in the feed gas to the ethylene oxide reactor system, the chloride make-up feed rate, or the catalyst chloride effectiveness value (Cl ｅｆｆ ), which is ##EQU00021## is calculated as whereby [MC], [EC], [EDC], and [VC] are the concentrations in ppmv of methyl chloride (MC), ethyl chloride (EC), ethylene dichloride (EDC), and vinyl chloride (VC), respectively, and [CH ４ ], [C ２ H ６ ] and [C ２ H ４ ] are the concentrations in mole percent of methane, ethane, and ethylene in the feed gas, respectively, and the regulator level (M) is set to its real-time level (M ｒｅａｌ－ｔｉｍｅ ) to its optimal level (M ｏｐｔ ) and ｅｆｆ The recommended change to bring the value of β-K to its optimal level of 0.0, expressed as a percentage, is: [0022] or as the equivalent incremental change in regulator level, or equivalently, and the absolute recommended optimal regulator level target is [0023] is defined as The real-time RCl ｅｆｆ [0024] but, (i) ΔS (ΔS ｒｅａｌ－ｔｉｍｅ ) and ΔT (ΔT ｒｅａｌ－ｔｉｍｅ ) and comparing said slope to a reference curve for the epoxidation catalyst; or (ii) From the approximation curve, the maximum ΔS (ΔS ｏｐｔ ) and the corresponding ΔT at the maximum ΔS (ΔT ｏｐｔ ) wherein the maximum ΔS is an optimal RCl ｅｆｆ determining, from the ΔS ｏｐｔ and the relative selectivity difference (RSD) is calculated by subtracting the ΔT from the ΔT ｏｐｔ Calculating the relative temperature difference (RTD) by subtracting the RSD (RSD ｒｅａｌ－ｔｉｍｅ ) and the RTD (RTD ｒｅａｌ－ｔｉｍｅ ) to a reference curve for the epoxidation catalyst, or by a combination of methods (i) and (ii) above; The reference curves are generated from previous laboratory tests, pilot plant tests, or early plant runs, and the selectivity deviation and temperature deviation versus optimum are plotted against the relative effective moderator level (RCI ｅｆｆ ) or by relating the slope of the plot of the selectivity deviation plotted against the temperature deviation to the relative effective moderator level (RCl ｅｆｆ ) to associate with the system.

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