A multi-stage distillation filtration method and system for the production of diphenyl monoisodecyl phosphite

Abstract

The application discloses a kind of multi-stage distillation filtration methods and systems of diphenyl monoisodecyl phosphite production, it is related to diphenyl monoisodecyl phosphite production and processing technical field, including the following steps: the ester exchange reaction after the crude product containing diphenyl monoisodecyl phosphite is carried out first thin film evaporation and is light, and the light component in the crude product after ester exchange reaction is separated out;The crude ester product after light removal is carried out secondary vacuum rectification treatment, and high-purity diphenyl monoisodecyl phosphite fraction is obtained;High-purity diphenyl monoisodecyl phosphite fraction is carried out third filtration purification treatment, and final product is obtained;By establishing the whole process integrated mathematical model covering thin film evaporation, vacuum rectification and filtration, system can real-time perception feed fluctuation, and each level process parameter is dynamically adjusted in advance, ensure that impurities are removed accurately and efficiently, effectively eliminate the product quality batch fluctuation caused by parameter fixed or adjustment lag under traditional mode.

Description

Technical Field

[0001] This invention relates to the field of production and processing technology of diphenyl isodecyl phosphite, and particularly to a multi-stage distillation and filtration method and system for the production of diphenyl isodecyl phosphite. Background Technology

[0002] Diphenyl isodecyl phosphite is an important organophosphorus compound, mainly used as an auxiliary antioxidant and auxiliary stabilizer in polymer materials. During processing, it can effectively decompose hydrogen peroxide, capture free radicals, and produce a synergistic effect with the main antioxidant, thereby significantly inhibiting the thermo-oxidative aging and color deterioration of materials, and improving the service life and appearance quality of products.

[0003] In industrial production, diphenyl isodecanyl phosphite is mainly synthesized by transesterification. The crude product contains unreacted raw materials, reaction byproducts, catalyst residues, and colored impurities produced by high temperature or oxidation. In order to obtain high-purity, low-color products that meet the requirements of high-end applications, the crude product must be deeply purified. Due to the variety of impurities and their different physicochemical properties, a single separation method is difficult to achieve the desired effect. Therefore, a multi-stage purification route combining distillation and filtration is commonly used in industry.

[0004] However, the control of multi-stage purification processes mainly relies on the experience of operators or relatively independent unit control. Each distillation and filtration unit is often set with fixed operating parameters or adjusted only based on simple feedback from its own unit. There is a lack of collaborative optimization from the perspective of optimizing overall product quality and economic benefits. This makes it difficult for the production process to adapt quickly to feed fluctuations, and product quality is prone to batch-to-batch fluctuations. To address this, a multi-stage distillation and filtration method for the production of diphenyl isodecanyl phosphite is proposed. Summary of the Invention

[0005] The main objective of this invention is to provide a multi-stage distillation and filtration method and system for the production of diphenyl isodecanyl phosphite, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A multi-stage distillation and filtration method for producing diphenyl isodecyl phosphite includes the following steps:

[0008] Step 1: The crude product containing diphenyl isodecanyl phosphite after transesterification reaction is subjected to primary thin-film evaporation to remove the light components from the crude product after transesterification reaction.

[0009] Step 2: The crude ester product after light ester removal is subjected to two-stage vacuum distillation to obtain high-purity diphenyl isodecanyl phosphite fraction;

[0010] Step 3: The high-purity diphenyl isodecyl phosphite fraction is subjected to a three-stage filtration purification process to obtain the final product;

[0011] The method further includes a real-time control step based on an integrated optimization model:

[0012] Construct a complete integrated mathematical model covering the thin-film evaporation unit, vacuum distillation unit, and filtration and purification unit;

[0013] Real-time acquisition of process operation parameters and product quality parameters of the above units;

[0014] Using the final product quality index as the core constraint and the production economic index as the objective function, rolling optimization calculations are carried out through the integrated mathematical model to dynamically adjust the key operation settings of each unit.

[0015] Furthermore, in step 1, the optimized control of the primary thin-film evaporation and light removal process includes:

[0016] Based on the constraints of light component removal rate and product thermal degradation rate, a unit optimization model is established with the goal of maximizing the throughput and minimizing energy consumption.

[0017] Real-time reception of feed composition and physical property parameters; dynamic adjustment of evaporation temperature by solving the unit optimization model. System operating pressure and feed rate .

[0018] Furthermore, the constraint on the thermal degradation rate of the product is determined based on a thermal degradation kinetic model, which is as follows:

[0019]

[0020] in, Concentration of undecomposed materials. The concentration of the decomposition products. , , For dynamic parameters, This is the universal gas constant. The residence time of the material in the evaporator is determined by the liquid film thickness model and the scraper rotation speed. and feed rate The connection is as follows:

[0021]

[0022]

[0023] in, For liquid film thickness, For material density, For evaporation area, The average molecular weight of the material. For material viscosity, , This is an empirical correlation parameter for liquid film thickness.

[0024] Furthermore, in step 2, the secondary vacuum distillation process employs model predictive control, and its optimization problem is:

[0025]

[0026] in, For the predicted tray temperature or pressure, Set a value for it. To manipulate variables, To predict the time domain, To control the time domain, , This is the weight matrix.

[0027] Furthermore, the product purity and impurity content are inferred in real time using a soft sensor model, which uses the temperature of the distillation column's sensitive trays as the basis for the measurement. Tower pressure Reflux ratio and feed composition As input, with the trained data-driven model as the kernel, the specific expression is:

[0028]

[0029] in, For a well-trained neural network model, To predict the purity of DPDIP in the reboiler, This represents the predicted impurity content of the distillate.

[0030] Furthermore, in step 3, the optimization control of the three-stage filtration and purification process includes: based on the real-time color value of the material before entering the filtration system. With acid value , and the preset threshold for colorimetry and acid value preset threshold The comparison determines whether to bypass the adsorption and filtration path. The path selection logic is as follows:

[0031]

[0032] Based on the adsorption kinetics model, with outlet color and acid value With compliance as a constraint and minimizing filtration cycle and adsorption energy consumption as objectives, the temperature of the adsorption filter is optimized through calculation. and cycle time The optimization formula is:

[0033]

[0034] The optimization formula is subject to the following constraints:

[0035]

[0036]

[0037]

[0038] in, These are the weighting coefficients. Energy consumption for the adsorption process, For export color, As an acid value standard, For adsorbents to remove impurities The load capacity, This represents the maximum loading capacity of the adsorbent.

[0039] Furthermore, the method also includes a predictive maintenance step based on a filter differential pressure growth model:

[0040] According to the model:

[0041]

[0042] Real-time monitoring of filtration differential pressure This is used to predict the filter cartridge's clogging status and remaining lifespan, and to issue a replacement reminder before the pressure differential reaches a warning threshold. , , For the blockage model parameters, The concentration of suspended solids. The molar flow rate of the filtered material.

[0043] A multi-stage distillation and filtration system for the production of diphenyl isodecanyl phosphite includes a thin-film evaporation unit, a vacuum distillation unit, and a filtration unit connected in sequence, as well as an integrated optimization control system. The integrated optimization control system includes: a data acquisition layer for real-time acquisition of process data and online analysis data from each unit; a model calculation layer, which deploys a full-process integrated mathematical model, unit optimization models for each unit, and control models; and an optimization execution layer for performing rolling optimization calculations and issuing optimized setpoint instructions to the basic controllers of each unit.

[0044] Furthermore, the thin-film evaporation unit is equipped with an online feed component analyzer and a viscometer; the vacuum distillation unit has temperature sensors installed on multiple theoretical plates; the inlet of the filtration unit is equipped with an online colorimeter and an acid value analyzer, and the filtration pipeline is equipped with an automatically switchable bypass valve.

[0045] Furthermore, the integrated optimization control system adopts a three-layer architecture, including: a top-level real-time optimization layer that periodically solves the whole-process economic optimization problem; a middle-level advanced process control layer that includes model predictive controllers for each unit; and a bottom-level conventional control layer that receives setpoints and executes basic loop control.

[0046] The present invention has the following beneficial effects:

[0047] 1. Compared with existing technologies, this solution establishes an integrated mathematical model covering the entire process of thin-film evaporation, vacuum distillation and filtration. The system can sense feed fluctuations in real time and dynamically adjust process parameters at each level in advance to ensure that impurities are removed accurately and efficiently step by step. This effectively eliminates batch-to-batch fluctuations in product quality caused by fixed parameters or delayed adjustments in traditional methods, and can better grasp the key quality indicators of the final product.

[0048] 2. Compared with existing technologies, this solution adopts a three-layer control architecture of real-time optimization, advanced process control and basic control, and performs forward-looking control based on model prediction. This enables the system to automatically handle complex multi-variable coupling relationships and realize fully automatic closed-loop management from global production scheme optimization to local dynamic adjustment. This reduces the dependence on operator experience and lowers the risk of human error. Attached Figure Description

[0049] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0050] Figure 2 This is a system structure diagram of an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] Example 1

[0053] like Figure 1As shown, a multi-stage distillation and filtration method for producing diphenyl isodecyl phosphite includes the following steps: Step 1: The crude product containing diphenyl isodecyl phosphite after the transesterification reaction undergoes a first-stage thin-film evaporation process to remove light components, separating the light components from the crude product; Step 2: The crude ester product after light component removal undergoes a second-stage vacuum distillation process to obtain a high-purity diphenyl isodecyl phosphite fraction; Step 3: The high-purity diphenyl isodecyl phosphite fraction undergoes a third-stage filtration purification process to obtain the final product; The method also includes a real-time control step based on an integrated optimization model: constructing a full-process integrated mathematical model covering the thin-film evaporation unit, vacuum distillation unit, and filtration purification unit; real-time acquisition of process operating parameters and product quality parameters of each unit; using the final product quality index as the core constraint and the production economic index as the objective function, rolling optimization calculations are performed through the integrated mathematical model to dynamically adjust the key operation settings of each unit.

[0054] Preferably, the crude product contains approximately 85% DPDIP, approximately 10% light components such as phenol and isodecanol, and the remainder are heavy component impurities; the thin-film evaporator has an evaporation area of ​​5 m², and the evaporation temperature is... =180℃, system pressure =5kPa, feed rate =200 kg / h;

[0055] Vacuum distillation is carried out in a vacuum distillation column with 30 trays, a top pressure of 1 kPa, and a reflux ratio of... =3, the temperature of the tower bottom is 210℃;

[0056] Filtration and purification are carried out through a series of precision filters and activated carbon adsorption columns;

[0057] Furthermore, in the primary thin-film evaporation unit, the system is dynamically adjusted based on the following unit optimization model:

[0058] The optimization control of the primary thin-film evaporation process for removing light components includes: establishing a unit optimization model based on constraints of light component removal rate and product thermal degradation rate, with the objective of maximizing throughput or minimizing energy consumption; receiving feed composition and physical property parameters in real time, and dynamically adjusting the evaporation temperature by solving the unit optimization model. System operating pressure and feed rate ;

[0059] Objective: To maximize throughput while ensuring that light component removal rate is ≥98% and DPDIP thermal degradation rate is ≤0.5%.

[0060] Real-time adjustment: When the online component analyzer detects that the content of light components in the feed increases from 10% to 12%, the model automatically solves and adjusts: evaporation temperature. Temperature increased from 180℃ to 185℃; feed rate The system pressure decreased from 200 kg / h to 190 kg / h. Keep the pressure at 5 kPa to achieve the removal rate target while controlling the degradation risk;

[0061] The constraint on the thermal degradation rate of the product is determined based on a thermal degradation kinetic model, which is as follows:

[0062]

[0063] in, Concentration of undecomposed materials. The concentration of the decomposition products. , , For dynamic parameters, This is the universal gas constant. The residence time of the material in the evaporator is determined by the liquid film thickness model and the scraper rotation speed. and feed rate The connection is as follows:

[0064]

[0065]

[0066] in, For liquid film thickness, For material density, For evaporation area, The average molecular weight of the material. For material viscosity, , The parameter is an empirical correlation of the liquid film thickness;

[0067] Furthermore, , , , , ;

[0068] Calculated using the liquid film thickness model , This ensures that degradation is controllable;

[0069] Preferably, the two-stage vacuum distillation process employs model predictive control, and its optimization problem is:

[0070]

[0071] in, For the predicted tray temperature or pressure, Set a value for it. To manipulate variables, To predict the time domain, To control the time domain, , This is the weight matrix;

[0072] Furthermore, the secondary distillation column employs an MPC controller, which performs optimization calculations every 5 minutes;

[0073] Preferably, product purity and impurity content are inferred in real time using a soft sensor model, wherein the soft sensor model uses the temperature of the distillation column's sensitive trays as the basis for measurement. Tower pressure Reflux ratio and feed composition As input, with the trained data-driven model as the kernel, the specific expression is:

[0074]

[0075] in, For a well-trained neural network model, To predict the purity of DPDIP in the reboiler, The predicted impurity content of the distillate;

[0076] Furthermore, in the distillation column, the input variable for the trained neural network soft sensor model is: the temperature of the sensitive tray. Tower pressure reflux ratio Feed composition (DPDIP 92%)

[0077] Output: Real-time inference of DPDIP purity in the reboiler , ;

[0078] Update frequency: Inferences are made every 2 minutes, replacing the delay of offline testing;

[0079] Preferably, the optimized control of the three-stage filtration and purification process includes: based on the real-time color value of the material before entering the filtration system. With acid value , and the preset threshold for colorimetry and acid value preset threshold The comparison determines whether to bypass the adsorption and filtration path. The path selection logic is as follows:

[0080]

[0081] Based on the adsorption kinetics model, with outlet color and acid value With compliance as a constraint and minimizing filtration cycle and adsorption energy consumption as objectives, the temperature of the adsorption filter is optimized through calculation. and cycle time The optimized formula is:

[0082]

[0083] The optimization formula is subject to the following constraints:

[0084]

[0085]

[0086]

[0087] in, These are the weighting coefficients. Energy consumption for the adsorption process, For export color, As an acid value standard, For adsorbents to remove impurities The load capacity, This represents the maximum loading capacity of the adsorbent.

[0088] Preferably, it also includes predictive maintenance steps based on a filter pressure differential growth model:

[0089] According to the model:

[0090]

[0091] Real-time monitoring of filtration differential pressure This is used to predict the filter cartridge's clogging status and remaining lifespan, and to issue a replacement reminder before the pressure differential reaches a warning threshold. , , For the blockage model parameters, The concentration of suspended solids. The molar flow rate of the filtered material.

[0092] Furthermore, when the predicted remaining lifespan is less than 24 hours, the system suggests replacing the filter cartridge after 8 hours.

[0093] Example 2

[0094] like Figure 2As shown, a multi-stage distillation and filtration system for the production of diphenyl isodecanyl phosphite includes a thin-film evaporation unit, a vacuum distillation unit, and a filtration unit connected in sequence, as well as an integrated optimization control system. The integrated optimization control system includes: a data acquisition layer for real-time acquisition of process data and online analysis data from each unit; a model calculation layer, which deploys a full-process integrated mathematical model, unit optimization models for each unit, and control models; and an optimization execution layer for performing rolling optimization calculations and issuing optimized setpoint instructions to the basic controllers of each unit.

[0095] The thin-film evaporation unit is equipped with an online feed component analyzer and a viscometer; the vacuum distillation unit is equipped with temperature sensors on multiple theoretical plates; the inlet of the filtration unit is equipped with an online colorimeter and an acid value analyzer, and the filtration pipeline is equipped with an automatically switchable bypass valve;

[0096] The optimization control system adopts a three-layer architecture, including: a top-level real-time optimization layer, which periodically solves the whole-process economic optimization problem; a middle-level advanced process control layer, which contains model predictive controllers for each unit; and a bottom-level conventional control layer, which receives setpoints and executes basic loop control.

[0097] Preferably, the thin-film evaporation unit includes a scraped thin-film evaporator (WFE), a feed preheater, a light component condenser, and a vacuum system;

[0098] Specifically, an online NIR analyzer and viscometer are installed on the feed line;

[0099] The vacuum distillation unit includes a packed distillation column, a top condenser, a bottom reboiler, a reflux tank, and a vacuum system. High-precision platinum resistance thermometers are installed at the equivalent positions of the 15th, 20th, and 25th theoretical plates in the column.

[0100] The filtration unit includes a jacketed adsorption filter tank, which is filled with activated alumina and special decolorizing clay with a precision of 1. The terminal membrane filter, circulation pump, and parallel pipeline with three-way valve are equipped with an online colorimeter and a potentiometric titration acid value analyzer installed at the main pipe inlet. Differential pressure transmitters are installed at the inlet and outlet of each filter.

[0101] Specifically, the data acquisition layer consists of the DCS's I / O modules and various online instruments, responsible for collecting all data such as temperature, pressure, flow rate, liquid level, composition, color, and acid value;

[0102] The model computation layer is hosted by a high-performance industrial real-time server;

[0103] The server runs a thin-film evaporator steady-state optimization module using algorithms jointly programmed and compiled with MATLAB and Python.

[0104] A rigorous dynamic model of the distillation column, MPC controller, filtration system optimization and lifetime prediction module, and full-process RTO optimization module are implemented using the Aspen Dynamics simulation engine and MPC toolkit.

[0105] The optimized execution layer receives the optimized setpoints from the model calculation layer and drives the actuator through the underlying PID control loop to achieve precise control.

[0106] It should be noted that after the system starts up, the quality target of the final product is first set on the DCS. The integrated optimization control system then enters the automatic operation mode. The data acquisition layer continuously sends data to the server, the modules of the model calculation layer perform parallel operations, and the optimization execution layer coordinates and controls each process unit. The entire process, from feeding materials to producing qualified products, does not require manual intervention or parameter adjustment. The system automatically seeks optimization and stabilizes at the best production state. When the properties of raw materials change or market conditions change, the system can automatically and quickly adjust to the new optimal operating point.

[0107] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A multi-stage distillation and filtration method for producing diphenyl isodecanyl phosphite, characterized in that, Includes the following steps: Step 1: The crude product containing diphenyl isodecanyl phosphite after transesterification reaction is subjected to primary thin-film evaporation to remove the light components from the crude product after transesterification reaction. Step 2: The crude ester product after light ester removal is subjected to two-stage vacuum distillation to obtain high-purity diphenyl isodecanyl phosphite fraction; Step 3: The high-purity diphenyl isodecyl phosphite fraction is subjected to a three-stage filtration purification process to obtain the final product; The method further includes a real-time control step based on an integrated optimization model: Construct a complete integrated mathematical model covering the thin-film evaporation unit, vacuum distillation unit, and filtration and purification unit; Real-time acquisition of process operation parameters and product quality parameters of the above units; Using the final product quality index as the core constraint and the production economic index as the objective function, rolling optimization calculations are carried out through the integrated mathematical model to dynamically adjust the key operation settings of each unit.

2. The multi-stage distillation and filtration method for producing diphenyl isodecanyl phosphite according to claim 1, characterized in that, In step 1, the optimization control of the primary thin-film evaporation and light removal process includes: Based on the constraints of light component removal rate and product thermal degradation rate, a unit optimization model is established with the goal of maximizing the throughput and minimizing energy consumption. The system receives the composition and physical properties of the feed in real time, and dynamically adjusts the evaporation temperature, system operating pressure, and feed rate by solving the unit optimization model.

3. The multi-stage distillation and filtration method for producing diphenyl isodecanyl phosphite according to claim 2, characterized in that, The constraint on the thermal degradation rate of the product is determined based on the thermal degradation kinetic model, and the residence time of the material in the evaporator is related to the scraper rotation speed and feed rate through the liquid film thickness model.

4. The multi-stage distillation and filtration method for producing diphenyl isodecanyl phosphite according to claim 1, characterized in that, In step 2, the secondary vacuum distillation process employs model predictive control.

5. The multi-stage distillation and filtration method for producing diphenyl isodecanyl phosphite according to claim 4, characterized in that, The purity and impurity content of the product are inferred in real time through a soft sensing model. The soft sensing model takes the temperature of the sensitive trays of the distillation column, the column pressure, the reflux ratio and the feed composition as inputs, and uses a trained data-driven model as its kernel.

6. The multi-stage distillation and filtration method for producing diphenyl isodecanyl phosphite according to claim 1, characterized in that, In step 3, the optimization control of the three-stage filtration and purification process includes: comparing the real-time color value and acid value of the material before entering the filtration system with a preset threshold, selecting whether to bypass the adsorption filtration path, and optimizing the calculation of the adsorption filter temperature and cycle time based on the adsorption kinetic model, with the outlet color and acid value meeting the standards as constraints and the filtration cycle and adsorption energy consumption as objectives.

7. The multi-stage distillation and filtration method for producing diphenyl isodecanyl phosphite according to claim 1, characterized in that, The method also includes a predictive maintenance step based on a filter differential pressure growth model: the filter differential pressure is monitored in real time according to the model to predict the filter element clogging status and remaining life, and a replacement prompt is issued before the differential pressure reaches the warning threshold.

8. A multi-stage distillation and filtration system for producing diphenyl isodecanyl phosphite using the method according to any one of claims 1-7, characterized in that, It includes a thin-film evaporation unit, a vacuum distillation unit, and a filtration unit connected in sequence, as well as an integrated optimization control system; The integrated optimization control system includes: The data acquisition layer is used to acquire process data and online analysis data from each unit in real time. The model calculation layer is equipped with a full-process integrated mathematical model, unit optimization models for each unit, and control models. The optimization execution layer is used to perform rolling optimization calculations and issue optimized setpoint instructions to the basic controllers of each unit.

9. A multi-stage distillation and filtration system for the production of diphenyl isodecanyl phosphite according to claim 8, characterized in that, The thin-film evaporation unit is equipped with an online feed component analyzer and a viscometer; The vacuum distillation unit is equipped with temperature sensors on multiple theoretical plates; The inlet of the filtration unit is equipped with an online colorimeter and an acid value analyzer, and the filtration pipeline is equipped with an automatically switchable bypass valve.

10. A multi-stage distillation and filtration system for the production of diphenyl isodecanyl phosphite according to claim 8, characterized in that, The integrated optimization control system adopts a three-layer architecture, including: a top-level real-time optimization layer, which periodically solves the whole-process economic optimization problem; The middle layer is the advanced process control layer, which includes model predictive controllers for each unit; The underlying conventional control layer is used to receive set values ​​and execute basic loop control.

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