Production process of low-molecular-weight bisphenol A type polycarbonate polyether polyol and intelligent gas control method

Abstract

The invention relates to the technical field of chemistry and chemical engineering, and particularly discloses a production process of low-molecular-weight bisphenol A type polycarbonate polyether polyol and an intelligent gas control method, the production process comprises a premixing step, a feeding step, a CO2 pressure maintaining step, a heating step and a circular reaction step, in the circular reaction process, the reaction temperature is monitored in real time, and the reaction time is shortened; and performing multi-dimensional time sequence analysis on the time sequence data of the reaction temperature by adopting an intelligent industrial control technology so as to excavate the time sequence change trend of the reaction temperature and the fluctuation information of the temperature change rate, and judging whether CO2 gas needs to be supplemented in the current temperature state or not through an intelligent classification algorithm by taking the fluctuation information as an adjustment basis. Therefore, the temperature fluctuation in the reaction process can be responded in real time, and the self-adaptive supplement control of the CO2 gas is realized to ensure that the temperature of the reaction system is maintained in a safe and efficient range, so that the problem of yellowing of the temperature-sensitive micromolecular polycarbonate polyether polyol product is solved.

Description

Technical Field

[0001] The present application relates to the field of chemical engineering technology, and more specifically, to a production process of low molecular weight bisphenol A type polycarbonate polyether polyol and a gas intelligent control method. Background Art

[0002] Polycarbonate polyether polyols are a class of polyols with carbonate groups in the molecule and hydroxyl groups at the ends of the molecular chain. In the preparation process of this material, carbon dioxide is used as one of the raw materials. Due to its low cost, easy access, non-toxicity and non-flammability, it is used as an effective means of chemically fixing carbon dioxide.

[0003] Low molecular weight bisphenol A type polycarbonate polyether polyol is a polycarbonate polyether polyol with a molecular weight of less than 3000 synthesized by using bisphenol A as a small molecule initiator. The process of synthesizing polycarbonate polyether polyol is mainly synthesized by using carbon dioxide and epoxy compounds through zinc-cobalt bimetallic catalysts. Chinese patent CN111378106 mentions that the activation energy of generating polymers is lower than that of generating cyclic carbonates, and the potential energy of cyclic carbonates is lower than that of polymers. Therefore, the reaction of generating cyclic carbonates is a thermodynamically favorable reaction, while the reaction of generating polycarbonates is a kinetically favorable reaction. If the temperature of the reaction process is too high, bisphenol A is prone to thermal decomposition, and the decomposition products are prone to react with intermediate products and by-products of the polymerization reaction to form yellow substances, such as the generation of small molecular substances such as dimethyl phthalate or quinone structures with chromophores; too high a reaction temperature will also lead to low carbonate chain content, wide molecular weight distribution, and high proportion of by-product cyclic carbonates. Therefore, accurate reaction heat control is the key to improving the main product-polycarbonate polyether polyol.

[0004] At present, in the process of synthesizing low molecular weight bisphenol A type polycarbonate polyether polyol, in order to solve the problem of drastic temperature fluctuations in the polymerization reaction, the process adopted in the prior art is to add the reactants in batches, thereby reducing the concentrated release of heat in the polymerization process, which reduces the efficiency of the production process.

[0005] In addition, as one of the important raw materials for synthesizing polycarbonate polyether polyols, CO2 not only reacts with epoxy compounds and initiators under the action of catalysts to generate target products, but also plays a cooling role to prevent overheating. In other words, the addition and circulation of CO2 gas can quickly absorb the heat released by the reaction, reduce the temperature of the reaction system, and evenly transfer heat, thereby preventing the yellowing and explosion of the product caused by excessive temperature. However, in traditional production processes, the addition control of CO2 gas mainly relies on artificially set temperature thresholds. This control method is relatively extensive and is easily affected by human operating errors, environmental fluctuations, and changes in equipment performance. It cannot be dynamically adjusted according to the actual reaction conditions, which may lead to low control accuracy, thereby affecting product quality and production efficiency.

[0006] Therefore, an optimized production process of low molecular weight bisphenol A polycarbonate polyether polyol and a gas intelligent control method are expected. Summary of the invention

[0007] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a new production process and gas intelligent control method for low molecular weight bisphenol A type polycarbonate polyether polyol. The production process is carried out in a tube-in-tube reaction device, including a premixing step, a feeding step, a CO2 pressure maintenance step, a heating step and a cyclic reaction step. During the cyclic reaction, the reaction temperature is monitored in real time, and the time series data of the reaction temperature is analyzed in multiple dimensions using intelligent industrial control technology to dig out the time series change trend of the reaction temperature and the fluctuation information of the temperature change rate. Then, based on this, it is used as an adjustment basis to determine whether CO2 gas needs to be supplemented under the current temperature state through an intelligent classification algorithm. In this way, the temperature fluctuations in the reaction process can be responded to in real time, and the adaptive supplement control of CO2 gas can be realized to ensure that the temperature of the reaction system is maintained within a safe and efficient range, thereby solving the yellowing problem of the product of temperature-sensitive small molecule polycarbonate polyether polyol.

[0008] Accordingly, according to one aspect of the present application, a production process of low molecular weight bisphenol A type polycarbonate polyether polyol is provided, wherein the production process of low molecular weight bisphenol A type polycarbonate polyether polyol is carried out in a tube-in-tube reaction device, wherein the reaction device comprises a premixing unit and a reaction zone, wherein the reaction zone comprises a reaction pipeline and a liquid circulation pump, wherein a jacket is provided on the outside of the reaction pipeline, and the liquid circulation pump is connected to the reaction pipeline via a connecting pipeline to allow the material to circulate in the reaction pipeline; wherein the production process comprises: a premixing step of mixing an epoxy compound, CO2, a small molecule initiator and a catalyst in the premixing unit; After the units are mixed, a premixture is obtained; a feeding step: the premixture is fed into the reaction zone at one time; a CO2 pressure maintenance step: after the feeding is completed, CO2 is introduced into the reaction zone until the pressure of the reaction zone is a first pressure value of 3-4 MPa, and maintained at the first pressure value; a heating step: turning on the liquid circulation pump, adjusting the temperature of the medium in the jacket, so as to heat the reaction pipe to 75-85°C, and during the heating process of the reaction pipe, controlling the temperature difference between the medium in the jacket and the temperature of the reaction pipe to not exceed 3°C; a circulating reaction step: after the heating is completed, keeping the liquid circulation pump turned on, monitoring the The temperature in the reaction pipeline, when the temperature of the reaction pipeline increases or decreases, the temperature of the medium in the jacket is correspondingly reduced or increased, and the reduced or increased temperature of the jacket medium is roughly equal to the increased or reduced temperature of the reaction pipeline, so that the material temperature is maintained at 75-85°C, and the temperature in the reaction pipeline is monitored, and based on the temperature in the reaction pipeline, it is determined whether to add purified CO2 gas of a second pressure value to the reaction zone, the gas temperature of the purified CO2 gas is 58°C-62°C; the second pressure value is 4-8MPa, and the second pressure value is greater than the first pressure value, wherein, based on the reaction pipeline The method comprises: obtaining a time queue of the temperature in the reaction pipe; performing multi-dimensional time series correlation feature extraction on the time queue of the temperature in the reaction pipe to obtain a time series correlation feature of the absolute value of the temperature in the reaction pipe and a time series correlation feature of the temperature variation in the reaction pipe; performing a dynamic-static joint analysis on the time series correlation feature of the absolute value of the temperature in the reaction pipe and the time series correlation feature of the temperature variation in the reaction pipe to obtain a dynamic-static time series joint feature of the reaction temperature; and determining whether to supplement the purified CO2 gas to the reaction zone based on the dynamic-static time series joint feature of the reaction temperature.

[0009] In the production process of the low molecular weight bisphenol A polycarbonate polyether polyol, a multi-dimensional time series correlation feature extraction is performed on the time queue of the temperature in the reaction pipe to obtain the time series correlation feature of the absolute temperature in the reaction pipe and the time series correlation feature of the temperature change in the reaction pipe, including: calculating the temperature gradient value of the temperature in the reaction pipe at each time point in the time queue of the temperature in the reaction pipe to obtain the time queue of the temperature gradient value; extracting the time series correlation feature of the absolute temperature in the reaction pipe from the time queue of the temperature in the reaction pipe; extracting the time series correlation feature of the temperature change in the reaction pipe from the time queue of the temperature gradient value.

[0010] In the production process of the low molecular weight bisphenol A polycarbonate polyether polyol, the time series correlation characteristics of the absolute temperature in the reaction pipe are extracted from the time series of the temperature in the reaction pipe, including: inputting the time series of the temperature in the reaction pipe into a time series feature extractor based on the RNN model to obtain the time series correlation characteristics of the absolute temperature in the reaction pipe.

[0011] In the production process of the low molecular weight bisphenol A type polycarbonate polyether polyol, a dynamic-static joint analysis is performed on the time series correlation characteristics of the absolute temperature in the reaction pipeline and the time series correlation characteristics of the temperature change in the reaction pipeline to obtain a dynamic-static joint time series characteristic of the reaction temperature, including: fine-grained feature time series decoupling of the time series correlation characteristics of the absolute temperature in the reaction pipeline and the time series correlation characteristics of the temperature change in the reaction pipeline to obtain a set of local time series correlation characteristics of the absolute temperature in the reaction pipeline and a set of local time series correlation characteristics of the temperature change in the reaction pipeline; The set of local time series correlation features of the absolute value of temperature in the reaction pipeline and the set of local time series correlation features of the temperature change in the reaction pipeline are screened for the best feature sub-component pairing to obtain the best {local time series correlation features of the absolute value of temperature in the reaction pipeline, local time series correlation features of the temperature change in the reaction pipeline} pairing set; the best {local time series correlation features of the absolute value of temperature in the reaction pipeline, local time series correlation features of the temperature change in the reaction pipeline} pairing set is subjected to the best sub-component feature pairing interactive fusion and feature cascade to obtain the dynamic-static time series joint features of the reaction temperature.

[0012] In the production process of the low molecular weight bisphenol A type polycarbonate polyether polyol, the set of local time series correlation features of the absolute quantity of temperature in the reaction tube and the set of local time series correlation features of the temperature change in the reaction tube are screened for the best feature subcomponent pairing to obtain the best {local time series correlation features of the absolute quantity of temperature in the reaction tube, local time series correlation features of the temperature change in the reaction tube} pairing set, including: for each local time series correlation feature of the absolute quantity of temperature in the reaction tube in the set of local time series correlation features of the absolute quantity of temperature in the reaction tube, the hyperbolic space distance measurement factor between it and each local time series correlation feature of the temperature change in the reaction tube in the set of local time series correlation features of the temperature change in the reaction tube is calculated, and the local time series correlation feature of the temperature change in the reaction tube corresponding to the smallest hyperbolic space distance measurement factor is selected as the pairing object to obtain the best {local time series correlation features of the absolute quantity of temperature in the reaction tube, local time series correlation features of the temperature change in the reaction tube} pairing set.

[0013] In the production process of the low molecular weight bisphenol A polycarbonate polyether polyol, the optimal sub-component feature pairing interaction fusion and feature cascade are performed on the set of the optimal {local timing correlation feature of the absolute value of temperature in the reaction pipeline, local timing correlation feature of the temperature change in the reaction pipeline} pairings to obtain the dynamic-static timing joint feature of the reaction temperature, including: performing feature multi-dimensional interaction on each optimal {local timing correlation feature of the absolute value of temperature in the reaction pipeline, local timing correlation feature of the temperature change in the reaction pipeline} pairing in the optimal {local timing correlation feature of the absolute value of temperature in the reaction pipeline, local timing correlation feature of the temperature change in the reaction pipeline} pairings to obtain a set of dynamic-static timing multi-dimensional interaction features of the reaction temperature; cascading the set of dynamic-static timing multi-dimensional interaction features of the reaction temperature to obtain the dynamic-static timing joint feature of the reaction temperature.

[0014] In the production process of the low molecular weight bisphenol A polycarbonate polyether polyol, each optimal {local timing correlation feature of the absolute value of the temperature in the reaction pipeline, local timing correlation feature of the temperature change in the reaction pipeline} pair in the set of the optimal {local timing correlation feature of the absolute value of the temperature in the reaction pipeline, local timing correlation feature of the temperature change in the reaction pipeline} pair is subjected to feature multi-dimensional interaction to obtain a set of dynamic-static timing multi-dimensional interaction features of the reaction temperature, including: respectively calculating the optimal {local timing correlation feature of the absolute value of the temperature in the reaction pipeline, local timing correlation feature of the temperature change in the reaction pipeline} pair The local time series correlation feature of the absolute temperature in the reaction pipe and the local time series correlation feature of the temperature change in the reaction pipe in the association feature pairing are added at the position point, subtracted at the position point and multiplied at the position point to obtain the first reaction temperature dynamic-static time series interaction feature, the second reaction temperature dynamic-static time series interaction feature and the third reaction temperature dynamic-static time series interaction feature; the weighted sum by position between the first reaction temperature dynamic-static time series interaction feature, the second reaction temperature dynamic-static time series interaction feature and the third reaction temperature dynamic-static time series interaction feature is calculated to obtain the reaction temperature dynamic-static time series multi-dimensional interaction feature.

[0015] In the production process of the above-mentioned low molecular weight bisphenol A polycarbonate polyether polyol, whether to add purified CO2 gas to the reaction zone is determined based on the dynamic-static timing joint characteristics of the reaction temperature, including: inputting the dynamic-static timing joint characteristics of the reaction temperature into a classifier-based valve controller to obtain a control instruction, and the control instruction is used to indicate whether to add purified CO2 gas to the reaction zone.

[0016] In the above-mentioned production process of low molecular weight bisphenol A type polycarbonate polyether polyol, a gas-liquid mixed heat conduction unit and a solid-liquid mixing unit are provided in the reaction pipeline, and the gas-liquid mixed heat conduction unit includes a dispersion sheet and a gas-liquid mass transfer zone, wherein the gas-liquid mixed heat conduction unit is used to redissolve CO2 in the mixed material with a low CO2 content in the reaction zone, and at the same time, heat exchange is performed in the gas-liquid mixed heat conduction unit; the solid-liquid mixing unit is used to disperse the solid catalyst and disperse the reaction heat.

[0017] According to another aspect of the present application, a gas intelligent control method is provided, which includes: obtaining a time queue of the temperature in a reaction pipe; performing multi-dimensional time series correlation feature extraction on the time queue of the temperature in the reaction pipe to obtain a time series correlation feature of an absolute value of the temperature in the reaction pipe and a time series correlation feature of a temperature change in the reaction pipe; performing a dynamic-static joint analysis on the time series correlation feature of the absolute value of the temperature in the reaction pipe and the time series correlation feature of the temperature change in the reaction pipe to obtain a dynamic-static time series joint feature of the reaction temperature; and determining whether to add purified CO2 gas to the reaction zone based on the dynamic-static time series joint feature of the reaction temperature.

[0018] Compared with the prior art, the production process and gas intelligent control method of low molecular weight bisphenol A type polycarbonate polyether polyol described in the present application include a premixing step, a feeding step, a CO2 pressure maintenance step, a heating step and a cyclic reaction step. During the cyclic reaction, the reaction temperature is monitored in real time, and the time series data of the reaction temperature are analyzed in multiple dimensions using intelligent industrial control technology to dig out the time series change trend of the reaction temperature and the fluctuation information of the temperature change rate. Then, based on this, it is used as the basis for adjustment to determine whether CO2 gas needs to be supplemented under the current temperature state through an intelligent classification algorithm. In this way, the temperature fluctuations during the reaction process can be responded to in real time, and the adaptive supplement control of CO2 gas can be realized to ensure that the temperature of the reaction system is maintained within a safe and efficient range, thereby solving the yellowing problem of the product of temperature-sensitive small molecule polycarbonate polyether polyol. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 This is an overall schematic diagram of the reaction device for producing low molecular weight polycarbonate polyether polyols according to an embodiment of the present application, wherein the black arrow represents the raw material flow, the green arrow represents the CO2 gas flow, the blue arrow represents the raw material and product mixture circulation flow, and the red arrow represents the product flow.

[0021] Figure 2 The flowchart is a production process of low molecular weight polycarbonate polyether polyol according to an embodiment of the present application.

[0022] Figure 3 The present invention is a flow chart for determining whether to feed purified CO2 gas into the reaction zone based on the temperature in the reaction pipe in the production process of low molecular weight bisphenol A polycarbonate polyether polyol according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0024] The present application proposes a production process and gas intelligent control method for a low molecular weight bisphenol A type polycarbonate polyether polyol. Specifically, the low molecular weight bisphenol A type polycarbonate polyether polyol is synthesized by using bisphenol A as a small molecule initiator, propylene oxide and CO2 under the action of a DMC catalyst. Among them, the small molecule initiator containing an active H-functional group is added to the reaction system to obtain a polycarbonate polyether polyol with a molecular weight of several thousand, so that the polymer structure contains both polycarbonate chain segments and polyether chain segments generated by homopolymerization of epoxy compounds, and has high Young's modulus and flexibility. Initiators usually participate in the initial stage of the polymerization reaction to generate active centers, such as free radicals, cations or anions, which further promote the connection of monomer molecules to form polymer chains. In some polymerization reactions, a portion of the initiator (for example, the free radical portion in the free radical initiator) may be integrated into the end of the polymer chain. Ideally, the initiator will be completely consumed during the reaction without significant residue. However, in actual applications, a small amount of initiator may remain in the final product. In particular, using bisphenol A as an initiator, the product obtained is a low molecular weight bisphenol A type polycarbonate polyether polyol, the aromatic ring and carbon oxygen chain structure in the molecule give the material specific rigidity and toughness, thereby effectively improving the structure and properties of the polymer, giving downstream products better mechanical properties and thermal stability, and playing an important role in the fields of adhesives, rubbers, surfactants, etc.

[0025] However, when bisphenol A is exposed to a temperature higher than 80°C for a long time, it is easy to generate a small molecule substance, dimethyl phthalate, which not only fails to form a reaction active center, but also leaves residues that turn the product yellow. Therefore, during the synthesis process in the reaction zone, the local uniformity of the temperature field in the reactor needs to be strictly controlled to prevent explosion and local high temperature from occurring, thereby reducing the decomposition of bisphenol A into small molecules.

[0026] Based on this, the present application proposes a new production process of low molecular weight bisphenol A type polycarbonate polyether polyol, which is carried out in a tube-in-tube reaction device, which includes a premixing zone, a reaction zone and a post-processing zone. By adopting a special tube-in-tube reaction device and finely controlling the temperature and CO2 pressure of the reaction process, the heat exchange and temperature control capabilities are improved, the solid-liquid-gas mixing efficiency is improved, and problems such as implosion are avoided. The low molecular weight bisphenol A type polycarbonate polyether polyol is synthesized in one step, without amplification effect, suitable for industrial large-scale production, solves the problem of yellowing of the product, effectively improves the overall conversion rate and safety, and reduces production costs.

[0027] like Figure 1 As shown, the reaction device includes a premixing zone, a reaction zone and a post-processing zone. The raw materials are premixed in the premixing unit, and the premixed raw materials are passed into the reaction zone. After the reaction is completed, the reaction products are passed into the post-processing zone.

[0028] Specifically, in a preferred embodiment, the reaction zone includes a feed port, a reaction pipeline, a liquid distributor, a liquid circulation pump and a carbon dioxide gas replenishment port. The reaction pipeline is provided with multiple, and the multiple reaction pipelines are arranged in parallel. A jacket is provided on the outside of each reaction pipeline, and a medium such as steam, water, and heat transfer oil is introduced into the jacket to control the temperature of the reaction pipeline. The space between the multiple reaction pipelines is filled with a medium, and an insulation layer is provided on the outside of the medium. The carbon dioxide gas replenishment port is connected to the reaction pipeline for replenishing carbon dioxide into the reaction pipeline. The feed end and the discharge end of the reaction pipeline are respectively connected to the liquid distributor and the liquid circulation pump through a connecting pipeline.

[0029] Further preferably, each reaction pipeline has the same material circulation path as the connecting pipeline and the liquid circulation pump. By independently controlling each reaction pipeline, when the local temperature of a reaction pipeline is too high or other undesirable conditions occur, it can be controlled separately in time to avoid affecting the entire reaction process.

[0030] Further preferably, a gas-liquid mixing heat conduction unit and a solid-liquid mixing unit are provided in the reaction pipeline. The gas-liquid mixing heat conduction unit includes a dispersion sheet and a gas-liquid mass transfer zone. The loop between the gas-liquid mixing heat conduction unit, the reaction pipeline and the liquid circulation pump together constitutes a gas-liquid mixing heat conduction path, so that the mixed material with a low CO2 content in the reaction zone dissolves CO2 again, and heat exchange is performed in the gas-liquid mixing heat conduction unit. A static mixer is provided in the solid-liquid mixing unit, and the loop between the static mixer, the reaction pipeline and the liquid circulation pump together constitutes a solid-liquid mixing path. This allows the solid catalyst to be evenly dispersed in the epoxy compound, while dispersing the generated reaction heat, reducing the temperature difference between the center temperature in the reaction pipeline and the tube wall, and avoiding the generation of an explosion point.

[0031] Further preferably, by setting a dispersion sheet, the reaction materials can be evenly dispersed and flow down in a water droplet shape at the end, increasing the mass transfer and heat transfer area. The static mixer and liquid circulation pump built into the coordinated reaction pipeline can replace the traditional stirring process to continuously mix the reaction materials during the circulation process. At the same time, the dispersion sheet can effectively enable the reaction materials to perform better gas-liquid mass transfer in the gas area and release excess heat.

[0032] Further preferably, a temperature sensor is provided inside the reaction pipe, and a density detector is provided at the outlet, and the jacket temperature is adjusted according to the material density and the temperature inside the reactor to control the reaction rate. Specifically, in one embodiment, a first temperature sensor (X) is provided in the middle of the reaction pipe, and a second temperature sensor (Y) is provided at the bottom, so as to more comprehensively monitor the temperature inside the reaction pipe.

[0033] Figure 2Flow chart of the production process of low molecular weight polycarbonate polyether polyol according to the embodiment of the present application. Figure 2 As shown, the production process of low molecular weight bisphenol A type polycarbonate polyether polyol provided in the embodiment of the present application includes the following steps: premixing step S1: after mixing the epoxy compound, CO2, small molecule initiator and catalyst in the premixing unit, a premix is ​​obtained; feeding step S2: the premix is ​​fed into the reaction zone at one time; CO2 pressure maintenance step S3: after the feeding is completed, CO2 is introduced into the reaction zone until the pressure of the reaction zone is a first pressure value of 3~4MPa, and maintained at the first pressure value; heating step S4: start the liquid circulation pump, adjust the temperature of the medium in the jacket, so as to heat the reaction pipe to 79~81°C, and during the heating process of the reaction pipe, control the temperature of the medium in the jacket The temperature difference with the reaction pipe does not exceed 3°C; Circulation reaction step S5: After the heating is completed, keep the liquid circulation pump in the open state, monitor the temperature in the reaction pipe, and when the temperature of the reaction pipe increases or decreases, correspondingly reduce or increase the temperature of the medium in the jacket, the lowered or increased temperature of the jacket medium is roughly equal to the increased or decreased temperature of the reaction pipe, so that the material temperature is maintained at 75~85°C, and monitor the temperature in the reaction pipe, and determine whether to add purified CO2 gas to the reaction zone based on the temperature in the reaction pipe, the gas temperature of the purified CO2 gas is 58°C~62°C, the second pressure value is 4~8MPa, and the second pressure value is greater than the first pressure value.

[0034] Specifically, in the premixing step S1, in the premixing zone, carbon dioxide is provided by a carbon dioxide storage tank and passed into a premixing unit, and the propylene oxide raw material enters the premixing unit in the carbon dioxide atmosphere through a metering pump. A fixed amount of small molecule initiator and catalyst are added to the premixing unit using a weighing bucket. After the raw materials are mixed in the premixing unit, a premix is ​​obtained. Preferably, the temperature of the premixing unit is 0-40°C, the pressure is 0.1-2MPa, and the premixing time is 1-2h. More preferably, the premixing time is 1h.

[0035] Specifically, the feeding step S2: feed the premix into the reaction zone through the feed port at one time. When the raw materials enter the reaction pipe, the reaction unit is controlled to meet: the temperature of the reaction pipe is 35-45°C, and the CO2 pressure is 0.1-2MPa. More preferably, during the feeding process, the temperature is low to avoid the initiator from decomposing into small molecules.

[0036] Specifically, the CO2 pressure maintenance step S3: when the raw materials are completely fed in, the feeding is completed. Carbon dioxide is supplemented through the carbon dioxide gas replenishment port to make the pressure in the reaction pipeline a first pressure value. Preferably, the first pressure value is 3MPa. When the pressure is too low, a solenoid valve is used to automatically introduce carbon dioxide from the carbon dioxide gas replenishment port to supplement the pressure.

[0037] Specifically, the temperature raising step S4: start the liquid circulation pump, adjust the temperature of the medium in the jacket, so as to raise the temperature of the reaction pipe to 75-85°C (for example, to 80°C), and during the temperature raising process of the reaction pipe, control the temperature difference between the medium temperature in the jacket and the temperature of the reaction pipe to be no more than 3°C. Preferably, the temperature difference between the medium temperature in the jacket and the temperature of the reaction pipe is controlled to be 3°C, and the temperature of the reaction pipe is slowly raised until the temperature of the reaction pipe reaches 75-85°C. More preferably, during the temperature raising process, the temperature measured by the first temperature sensor X installed in the middle of the reaction pipe is used as the temperature of the reaction pipe.

[0038] Specifically, the circulating reaction step S5: after heating to 75-85°C, keep the liquid circulation pump turned on, monitor the temperature in the reaction pipeline, and when the temperature of the reaction pipeline increases or decreases, the medium temperature in the jacket is correspondingly reduced or increased, and the reduced or increased temperature of the jacket medium is roughly equal to the increased or reduced temperature of the reaction pipeline, so that the material temperature is maintained at 75-85°C. Specifically, in the above process, the temperature value of the second temperature sensor installed at the bottom of the reaction pipeline is used as the temperature of the reaction pipeline. The reaction pipeline often heats up due to the exothermic reaction of the raw materials. When heating up, the temperature of the medium in the jacket is reduced, so that the temperature of the reaction system is kept relatively constant.

[0039] At the same time, in the above-mentioned cyclic reaction process, when a more violent reaction occurs, or when the reaction rate in a local area is too fast, the reaction will release a large amount of heat, which may cause the temperature to rise rapidly, causing the reactants to decompose or turn yellow and discolor, and may even cause an implosion phenomenon, that is, the reaction is too violent, causing the reaction products to quickly aggregate to form large particles, thereby affecting the molecular weight distribution and physical properties of the product. As one of the important raw materials for synthesizing polycarbonate polyether polyols, CO2 not only reacts with epoxy compounds and initiators under the action of catalysts to generate target products, but also plays a cooling role to prevent overheating. In other words, the replenishment and circulation of CO2 gas can quickly absorb the heat released by the reaction, reduce the temperature of the reaction system, and evenly transfer heat, thereby preventing the product from turning yellow and implosion caused by excessive temperature. Based on this, in the technical solution of the present application, by monitoring the temperature in the reaction pipeline, it is determined whether to replenish the purified CO2 gas into the reaction zone. In a specific example of the present application, when the temperature in the reaction pipe is monitored to be higher than 85°C, the purified CO2 gas provided by the carbon dioxide purification unit is fed into the reaction zone through the carbon dioxide gas supply port until the temperature of the reaction pipe is reduced to 75~85°C, wherein when the purified CO2 gas is fed, the gas temperature is 58~62°C, and the pressure in the reaction zone is set to a second pressure value, which is greater than the first pressure value. Preferably, the gas temperature is 60°C and the second pressure value is 4~8MPa. For example, in one embodiment, the first pressure value is 3MPa and the second pressure value is 4MPa. When the temperature rises sharply above 85°C, the gas-liquid mass transfer degree is increased while lowering the temperature by feeding in purified CO2 gas.

[0040] More preferably, since the traditional method of controlling the replenishment of purified CO2 gas based on temperature threshold has certain limitations, it cannot accurately respond to temperature fluctuations during the reaction process. Therefore, in order to improve the automation and intelligence of the replenishment of purified CO2 gas, in a preferred embodiment of the present application, an artificial intelligence technology based on deep learning is used to perform a time series analysis of the temperature in the reaction pipeline, and the multi-dimensional time series correlation change characteristics of the temperature inside the reaction pipeline are extracted, so as to perform adaptive replenishment control of purified CO2 gas based on the dynamic change pattern of the temperature in the tube. In this way, it is possible to respond to temperature fluctuations during the reaction process in real time, thereby further improving the stability of the reaction process.

[0041] Figure 3 This is a flow chart of determining whether to add purified CO2 gas to the reaction zone based on the temperature in the reaction pipe in the production process of low molecular weight bisphenol A type polycarbonate polyether polyol according to an embodiment of the present application. Figure 3As shown, whether to add purified CO2 gas to the reaction zone is determined based on the temperature in the reaction pipe, including: S510, obtaining the time queue of the temperature in the reaction pipe; S520, performing multi-dimensional time series correlation feature extraction on the time queue of the temperature in the reaction pipe to obtain the time series correlation feature of the absolute temperature in the reaction pipe and the time series correlation feature of the temperature change in the reaction pipe; S530, performing dynamic-static joint analysis on the time series correlation feature of the absolute temperature in the reaction pipe and the time series correlation feature of the temperature change in the reaction pipe to obtain the dynamic-static time series joint feature of the reaction temperature; S540, determining whether to add purified CO2 gas to the reaction zone based on the dynamic-static time series joint feature of the reaction temperature.

[0042] Specifically, first, the time sequence of the temperature in the reaction pipeline is obtained. In the step S510, the time sequence of the temperature in the reaction pipeline is collected by a first temperature sensor X installed in the middle of the reaction pipeline.

[0043] Next, deep learning technology is further used to perform time series analysis on the time queue of the temperature in the reaction pipeline to capture the dynamic fluctuation pattern of the reaction temperature. It is worth mentioning that in order to improve the accuracy and comprehensiveness of data analysis, in step S520, a multi-level time series analysis method is used to process the time queue of the temperature in the reaction pipeline, and the absolute time series correlation characteristics and relative change time series correlation characteristics of the temperature inside the reaction pipeline are extracted respectively to reveal the time series change trend and temperature change rate of the actual value of the temperature inside the reaction pipeline, so as to more comprehensively and meticulously describe the temperature behavior in the reaction process, so as to achieve more precise control and optimization.

[0044] Specifically, the step S520 includes: calculating the temperature gradient value of the temperature in the reaction pipe at each time point in the time queue of the temperature in the reaction pipe to obtain a time queue of the temperature gradient value; extracting the time series correlation characteristics of the absolute value of the temperature in the reaction pipe from the time queue of the temperature in the reaction pipe; and extracting the time series correlation characteristics of the temperature change in the reaction pipe from the time queue of the temperature gradient value.

[0045] In a specific implementation, first, by calculating the temperature gradient value of the temperature in the reaction conduit at each time point in the time queue of the temperature in the reaction conduit, the rate of change of the reaction temperature is determined, thereby obtaining the time queue of the temperature gradient value. It should be understood that a sudden change in the temperature gradient value may be a sign of an abnormal situation during the reaction process. Without considering the addition of purified CO2 gas, if the temperature gradient value suddenly increases, it may indicate that the reaction is too intense and immediate measures need to be taken to cool down. On the contrary, if the temperature gradient value suddenly decreases, it may indicate that the reaction rate decreases and the reaction conditions need to be adjusted to maintain the normal progress of the reaction. In a specific example of the present application, the temperature difference of adjacent time points in the time queue of the temperature in the reaction conduit is calculated by a simple difference method and divided by the sampling period to obtain the temperature gradient value at each time point to obtain the time queue of the temperature gradient value.

[0046] Then, the time queue of the temperature in the reaction pipeline and the time queue of the temperature gradient value are further time-series encoded using a machine learning model. In a specific example of the present application, the time queue of the temperature in the reaction pipeline is input into a time series feature extractor based on an RNN model to obtain the time series correlation feature of the absolute amount of temperature in the reaction pipeline. It should be understood that the RNN model is a neural network that is particularly suitable for processing time series data. In the technical solution of the present application, the time series dependency in the time queue of the temperature in the reaction pipeline is captured by using the cyclic connection structure inside the RNN model, which can fully mine the time series variation pattern of the temperature in the reaction pipeline, thereby extracting the absolute amount of captured temperature. Time series correlation features. Correspondingly, for the time queue of the temperature gradient value, the time series feature extractor based on the RNN model is also used to perform time series encoding on it to ensure the consistency and accuracy of feature extraction, thereby obtaining the time series correlation feature of the temperature change in the reaction pipeline, providing more comprehensive data support for real-time monitoring and control of the reaction process.

[0047] After the above processing, the time series correlation characteristics of the absolute temperature in the reaction pipeline mainly describe the changing trend of the actual value of the temperature (static level) during the reaction process, while the time series correlation characteristics of the temperature change in the reaction pipeline reveal the fluctuation of the temperature change rate (dynamic change) during the reaction process. Therefore, in order to comprehensively consider the multi-level temperature information to provide a more comprehensive temperature description, it is necessary to further perform a dynamic-static joint analysis on the time series correlation characteristics of the absolute temperature in the reaction pipeline and the time series correlation characteristics of the temperature change in the reaction pipeline to obtain a comprehensive temperature feature representation. In particular, in step S530 described in the present application, fine-grained feature decoupling and sub-component pairing screening are performed on the time series correlation characteristics of the absolute temperature in the reaction pipeline and the time series correlation characteristics of the temperature change in the reaction pipeline to optimize the feature fusion effect and enhance the effectiveness of the feature representation. Specifically, in order to improve the accuracy of feature interaction, firstly, fine-grained feature decoupling is performed on the time series correlation features of the absolute temperature in the reaction pipe and the time series correlation features of the temperature change in the reaction pipe based on a predetermined scale, and they are decomposed into multiple sub-components to form a set of local time series correlation features of the absolute temperature in the reaction pipe and a set of local time series correlation features of the temperature change in the reaction pipe, so as to facilitate feature interaction at a lower level and refine the granularity of feature interaction. After feature decoupling, the hyperbolic space distance measurement method is further used to calculate the hyperbolic space distance measurement factor between any local time series correlation features of the absolute temperature in the reaction pipe and the local time series correlation features of the temperature change in the reaction pipe, so as to capture the feature correlation between the two, thereby providing guidance for subsequent optimal feature matching interaction. Then, based on the hyperbolic space distance measurement factor, the set of local time series correlation features of the absolute temperature in the reaction pipeline and the set of local time series correlation features of the temperature change in the reaction pipeline are screened for the best feature sub-component pairing to screen out a pair of feature vectors with the smallest distance in the hyperbolic space, thereby ensuring that the subsequent interactive fusion process retains the most valuable feature information and achieves the best match. Furthermore, by performing multi-dimensional interaction on each of the selected best pairs, a deeper feature association between the two is learned to enhance the feature expression ability. Finally, the interaction results of all the best pairs are cascaded to integrate the interactive information of all sub-components, thereby generating a global dynamic-static time series joint feature of the reaction temperature, thereby providing a comprehensive description of the temperature behavior of the reaction process.

[0048] In a specific example of the present application, the step S530 includes: performing fine-grained feature timing decoupling on the timing correlation features of the absolute temperature in the reaction pipe and the timing correlation features of the temperature change in the reaction pipe to obtain a set of local timing correlation features of the absolute temperature in the reaction pipe and a set of local timing correlation features of the temperature change in the reaction pipe; performing optimal feature sub-component pairing screening on the set of local timing correlation features of the absolute temperature in the reaction pipe and the set of local timing correlation features of the temperature change in the reaction pipe to obtain an optimal set of {local timing correlation features of the absolute temperature in the reaction pipe, local timing correlation features of the temperature change in the reaction pipe} pairs; performing optimal sub-component feature pairing interactive fusion and feature cascading on the optimal set of {local timing correlation features of the absolute temperature in the reaction pipe, local timing correlation features of the temperature change in the reaction pipe} pairs to obtain the dynamic-static timing joint features of the reaction temperature.

[0049] Specifically, the set of local time series correlation features of the absolute quantity of temperature in the reaction pipe and the set of local time series correlation features of the temperature change in the reaction pipe are screened for the best feature sub-component pairing to obtain the optimal set of {local time series correlation features of the absolute quantity of temperature in the reaction pipe, local time series correlation features of the temperature change in the reaction pipe} pairs, including: for each local time series correlation feature of the absolute quantity of temperature in the reaction pipe in the set of local time series correlation features of the absolute quantity of temperature in the reaction pipe, calculating the hyperbolic space distance measurement factor between it and each local time series correlation feature of the temperature change in the reaction pipe in the set of local time series correlation features of the temperature change in the reaction pipe, and selecting the local time series correlation feature of the temperature change in the reaction pipe corresponding to the smallest hyperbolic space distance measurement factor as the pairing object to obtain the optimal set of {local time series correlation features of the absolute quantity of temperature in the reaction pipe, local time series correlation features of the temperature change in the reaction pipe} pairs.

[0050] Specifically, the set of the optimal {local time series correlation features of the absolute value of temperature in the reaction pipeline, local time series correlation features of the temperature change in the reaction pipeline} pairs is subjected to optimal sub-component feature pairing interactive fusion and feature cascade to obtain the reaction temperature dynamic-static time series joint features, including: performing feature multi-dimensional interaction on each optimal {local time series correlation features of the absolute value of temperature in the reaction pipeline, local time series correlation features of the temperature change in the reaction pipeline} pair in the set of the optimal {local time series correlation features of the absolute value of temperature in the reaction pipeline, local time series correlation features of the temperature change in the reaction pipeline} pairs to obtain a set of reaction temperature dynamic-static time series multi-dimensional interactive features; cascading the set of reaction temperature dynamic-static time series multi-dimensional interactive features to obtain the reaction temperature dynamic-static time series joint features.

[0051] More specifically, for each optimal {local time series correlation feature of the absolute value of temperature in the reaction pipeline, local time series correlation feature of the temperature change in the reaction pipeline} pair in the set of the optimal {local time series correlation feature of the absolute value of temperature in the reaction pipeline, local time series correlation feature of the temperature change in the reaction pipeline} pair, feature multi-dimensional interaction is performed to obtain a set of dynamic-static time series multi-dimensional interaction features of the reaction temperature, including: respectively calculating the reaction in the optimal {local time series correlation feature of the absolute value of temperature in the reaction pipeline, local time series correlation feature of the temperature change in the reaction pipeline} pair The local time series correlation characteristics of the absolute temperature in the pipeline and the local time series correlation characteristics of the temperature change in the reaction pipeline are added, subtracted and multiplied by position points to obtain the first reaction temperature dynamic-static time series interaction characteristics, the second reaction temperature dynamic-static time series interaction characteristics and the third reaction temperature dynamic-static time series interaction characteristics; the weighted sum by position between the first reaction temperature dynamic-static time series interaction characteristics, the second reaction temperature dynamic-static time series interaction characteristics and the third reaction temperature dynamic-static time series interaction characteristics is calculated to obtain the reaction temperature dynamic-static time series multi-dimensional interaction characteristics.

[0052] Accordingly, the step S530 includes: processing the absolute temperature time series correlation feature in the reaction pipeline and the temperature change time series correlation feature in the reaction pipeline using the following feature fine-grained matching interaction formula to obtain the dynamic-static time series joint feature of the reaction temperature, wherein the feature fine-grained matching interaction formula is:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] in, represents the time series correlation characteristics of the absolute value of the temperature in the reaction pipeline, represents the time series correlation characteristics of the temperature variation in the reaction pipeline, Indicates decoupling processing, , , and Respectively represent the first, second, and and The local time series correlation characteristics of the absolute temperature in the reaction pipeline, The value of is the number of local time series correlation features of the absolute temperature in the reaction pipeline, , , , and Respectively represent the first, second, and , and The local time series correlation characteristics of the temperature variation in each reaction pipeline, represents the inverse hyperbolic cosine function in hyperbolic space, represents the norm of the eigenvector, Indicates the The local time series correlation characteristics of the absolute temperature in the reaction pipeline and the The hyperbolic space distance measurement factor between the local temporal correlation features of the temperature variation in the reaction pipeline, It means to find the index of the local time series correlation feature of the temperature change in the reaction pipeline that minimizes the hyperbolic space distance measurement factor. The index of the local time series correlation feature of the temperature variation in the reaction pipeline corresponding to the minimum hyperbolic space distance measurement factor, , and For different weight parameters, represents dot product, Indicates point addition, Indicates point reduction, , , and Respectively represent the first, second, and and The multi-dimensional interactive characteristics of the dynamic-static time series of reaction temperature, Indicates cascade operation, It represents the dynamic-static time series joint characteristics of the reaction temperature.

[0059] Finally, based on the dynamic-static timing joint feature of the reaction temperature, determine whether to add purified CO2 gas to the reaction zone. In a specific example of the present application, the step S540 includes: inputting the dynamic-static timing joint feature of the reaction temperature into a valve controller based on a classifier to obtain a control instruction, and the control instruction is used to indicate whether to add purified CO2 gas to the reaction zone. That is, a valve controller is constructed using a classifier model to achieve control of the addition of purified CO2 gas. Specifically, the classifier model can achieve a comprehensive analysis and understanding of the temperature change of the reaction process by learning the characteristic pattern of the dynamic-static timing joint feature of the input reaction temperature, thereby outputting the corresponding classification results and accurately judging whether it is necessary to add purified CO2 gas under the current temperature conditions to ensure the stable progress of the reaction process. In this way, real-time monitoring and adaptive regulation of the reaction process can be achieved to ensure that the reaction process is carried out within the optimal temperature range, thereby improving the reaction efficiency and product quality.

[0060] In the case where the time series correlation feature of the absolute temperature in the reaction pipe and the time series correlation feature of the temperature change in the reaction pipe respectively represent the time series correlation feature of the absolute temperature in the reaction pipe and the time series correlation feature of the temperature change in the reaction pipe, when performing fine-grained feature matching interaction, considering that the temperature change in the reaction pipe is calculated from the absolute temperature in the reaction pipe, the time series correlation feature of the absolute temperature in the reaction pipe and the time series correlation feature of the temperature change in the reaction pipe have feature redundancy and manifold stacking in the high-dimensional feature space, resulting in feature distribution diversity in the fine-grained matching interaction features. Therefore, when the reaction temperature dynamic-static time series joint feature is input into the valve controller based on the classifier for classification, the accuracy of the classification judgment result is affected.

[0061] Preferably, when the dynamic-static time series joint feature of the reaction temperature is input into the valve controller based on the classifier for classification probability regression, the dynamic-static time series joint feature of the reaction temperature is first optimized, and the optimization process specifically includes: Determine the mean and variance of the feature set composed of the feature values ​​of the reaction temperature dynamic-static time series joint feature, and divide the mean by the variance to obtain the reaction temperature dynamic-static time series joint probability weight value:

[0062] in, and represent the mean and variance of the feature set composed of the feature values ​​of the dynamic-static time series joint feature of the reaction temperature, respectively, Represents the joint probability weight value of the dynamic-static time series of the reaction temperature; The reaction temperature dynamic-static time series joint feature point is multiplied by the inverse of the maximum eigenvalue of the reaction temperature dynamic-static time series joint feature to obtain the reaction temperature dynamic-static time series joint probability constraint vector:

[0063] in, represents the dynamic-static time series joint characteristics of the reaction temperature, represents the maximum eigenvalue of the dynamic-static time series joint feature of the reaction temperature, It means point multiplication by position. represents the joint probability constraint vector of the dynamic-static time series of reaction temperature; The reaction temperature dynamic-static time series joint probability constraint vector is added with the reaction temperature dynamic-static time series joint probability weight value, and the logarithmic value with base 2 is calculated to obtain the reaction temperature dynamic-static time series joint information interaction vector:

[0064] in, represents the joint probability constraint vector of the dynamic-static time series of reaction temperature, represents the joint probability weight value of the dynamic-static time series of the reaction temperature, Indicates adding by position point. Represents the dynamic-static time series joint information interaction vector of reaction temperature; After subtracting the reaction temperature dynamic-static time series joint probability constraint vector from the reaction temperature dynamic-static time series joint probability weight value, the inverse of each eigenvalue is calculated to obtain the reaction temperature dynamic-static time series joint sequence constraint vector:

[0065] in, represents the joint probability constraint vector of the dynamic-static time series of reaction temperature, represents the joint probability weight value of the dynamic-static time series of the reaction temperature, It means to subtract by position point. represents the dynamic-static joint sequence constraint vector of reaction temperature; Finally, the reaction temperature dynamic-static timing joint information interaction vector is added to the reaction temperature dynamic-static timing joint sequence constraint vector to obtain an optimized reaction temperature dynamic-static timing joint feature.

[0066] The above-mentioned optimization process simulates the mesoscale interaction structure under probability constraints between the eigenvalue scale and the eigenvector scale of the reaction temperature dynamic-static time series joint characteristics through the probability statistical characteristics of the reaction temperature dynamic-static time series joint characteristics, so as to construct a bidirectional latent variable motif based on the mesoscale short sequence relative to the probability statistical value to perform mesoscale bidirectional migration, and perform a posteriori recovery based on the sequence constraints on the interactive information, thereby improving the convergence effect of the reaction temperature dynamic-static time series joint characteristics in the probability density domain and improving the accuracy of the control instructions obtained by the valve controller based on the classifier.

[0067] Further preferably, in the above reaction process, the density is taken as the reaction end point, and when the density reaches 1.1-1.3 g / cm 3 More preferably, the reaction is terminated when the density reaches 1.12 g / cm 3 The reaction is terminated.

[0068] Further preferably, in the above reaction process, the reaction mixture product at the discharge end of the reaction pipeline converges to the liquid circulation pump via a connecting pipeline at one end, and converges to the liquid distributor at the top of the reaction pipeline through the liquid circulation pump and the connecting pipeline at the other end, and then passes through the reaction pipeline solid-liquid mixing unit and the gas-liquid mixing heat conduction unit to complete a cycle. The circulation flow rate of the liquid circulation pump is 1 to 20 minutes to complete a cycle. Further preferably, the circulation flow rate of the liquid circulation pump is 10 to 15 minutes to complete a cycle. More preferably, the circulation fluidity of the liquid circulation pump is 10 minutes to complete a cycle. By regulating the circulation flow rate of the liquid circulation pump, it is ensured that the reaction process is fully carried out in heat conduction, gas-liquid mass transfer, solid-liquid mixing and other processes, and the reaction process is ensured to be stable, and the quality and production efficiency of the product are improved.

[0069] After the reaction is completed, the reaction product (the reaction product is a mixture containing polycarbonate polyether polyol, cyclic carbonate and unreacted raw material propylene oxide) flows out from the discharge port. The single discharge of the discharge port is not more than 80% of the volume of the reaction pipeline. The reaction product is then passed into the separation unit in the post-processing area, and the separation unit separates the propylene oxide and dissolved CO2 in the mixture to obtain a crude product, and adjusts the viscosity of the crude product at the same time. The crude product is passed into the refining unit, and a refining agent is added for adsorption treatment. After filtering, a crude product with a catalyst metal content of less than 5ppm is obtained. The refined crude product is passed into the distillation unit to separate the main product polycarbonate polyether polyol and the by-product cyclic carbonate, that is, a polycarbonate polyether polyol product is obtained.

[0070] It should be noted that the above-mentioned premixing unit, separation unit, refining unit, distillation unit, etc. can all refer to the existing structure and will not be described in detail here.

[0071] Correspondingly, the present application also provides a gas intelligent control method, which includes: obtaining a time queue of the temperature in a reaction pipe; performing multi-dimensional time series correlation feature extraction on the time queue of the temperature in the reaction pipe to obtain a time series correlation feature of the absolute value of the temperature in the reaction pipe and a time series correlation feature of the temperature change in the reaction pipe; performing a dynamic-static joint analysis on the time series correlation feature of the absolute value of the temperature in the reaction pipe and the time series correlation feature of the temperature change in the reaction pipe to obtain a dynamic-static time series joint feature of the reaction temperature; and determining whether to add purified CO2 gas to the reaction zone based on the dynamic-static time series joint feature of the reaction temperature.

[0072] Here, those skilled in the art can understand that the specific operations of each step in the above-mentioned gas intelligent control method have been described in detail in the description of the production process of the above-mentioned low molecular weight bisphenol A polycarbonate polyether polyol, and therefore, its repeated description will be omitted.

[0073] In summary, the production process and gas intelligent control method of low molecular weight bisphenol A type polycarbonate polyether polyol according to the embodiment of the present application are explained. The production process is carried out in a tube-in-tube reaction device, including a premixing step, a feeding step, a CO2 pressure maintenance step, a heating step and a cyclic reaction step. During the cyclic reaction, the reaction temperature is monitored in real time, and the time series data of the reaction temperature are analyzed in a multi-dimensional time series by using intelligent industrial control technology to dig out the time series change trend of the reaction temperature and the fluctuation information of the temperature change rate. Then, based on this, it is used as the basis for adjustment to determine whether CO2 gas needs to be supplemented under the current temperature state through an intelligent classification algorithm. In this way, the temperature fluctuations during the reaction process can be responded to in real time, and the adaptive supplement control of CO2 gas can be realized to ensure that the temperature of the reaction system is maintained within a safe and efficient range, thereby solving the yellowing problem of the product of temperature-sensitive small molecule polycarbonate polyether polyol.

[0074] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0075] Example 1 This embodiment provides a low molecular weight bisphenol A type polycarbonate polyether polyol, using the above-mentioned reaction device, the reaction process is as follows: 8L propylene oxide, 4g DMC catalyst, and 224g bisphenol A were added to the premixing kettle respectively, and 0.4Mpa carbon dioxide was introduced and mixed and stirred at a temperature of 40°C. After stirring for 1h, the mixture was pressed into an 8L×2 tube reactor, and the diameter of a single reaction pipe was 100mm and the length of the reaction pipe was 1000mm. After all the mixture was pressed into the reactor, carbon dioxide was introduced into the reaction pipe to 3Mpa and the pressure was maintained. The liquid circulation pump was turned on, and the flow rate was controlled to complete a cycle in 11min (the flow rate of the liquid circulation pump was about 600g / min). The temperature difference between the medium temperature in the jacket of the reaction pipe and the temperature in the reactor was controlled to be 5°C, and the temperature in the reactor was slowly increased (temperature sensor X) until the raw material temperature in the reaction unit was increased to 80°C. During the cyclic reaction process, when the raw material temperature rises due to the exothermic reaction, the jacket temperature is reduced, and the difference between the reduced temperature and the temperature sensor (X) greater than 80°C is equal, so that the material temperature is kept balanced at 80°C. In this process, when the temperature in the reaction pipeline is greater than 85°C, CO2 is introduced from the CO2 purification system, the gas temperature is 60°C, and the pressure is supplemented to 4MPa until the temperature in the reaction pipeline drops to 80°C. After reacting at 80°C for 3 hours, the density is uniformly increased to 1.12g / cm 3 The reaction is complete and the temperature is lowered to remove the product. After the catalyst is removed by the catalyst filter, the main and by-products are separated by a scraper evaporator.

[0076] The DMC catalyst is a zinc-cobalt double metal cyanide complex catalyst obtained by reacting water-soluble metal salts of zinc and cobalt in a water-soluble solvent. The specific preparation method is as follows: potassium hexacyanocobaltate and zinc bromide in a molar ratio of 1:4 are weighed, dissolved in an aqueous solvent containing water and tert-butyl alcohol, and stirred continuously, and the total mass of the metal salt (i.e., cobalt salt and zinc salt) and the mass ratio of the aqueous solvent are 1:5. Inorganic acid and organic acid are added, the inorganic acid is dilute hydrochloric acid, the pH value is 2, the organic acid is glutaric acid, the molar ratio of the inorganic acid to the organic acid is 5:1, and the total molar number of the metal salt to the molar number of the acid is 4:1. Stir for several hours at a temperature of 10-100°C, and precipitate is continuously generated. The above precipitate is filtered and dried to obtain a filter cake. The filter cake is re-slurried and washed with an aqueous solvent at a temperature of 10-100°C, specifically, the washing temperature is 100°C, washing for 3 minutes, stirring for several hours, and then filtered and dried to obtain a filter cake, and the above slurrying, washing, and drying steps are repeated for multiple times at a temperature of 10-100°C until the system liquid pH is 6-7, specifically, the temperature is 60°C, and each time for 6 minutes. The solid product is further dried under vacuum conditions at 80-100°C to obtain the final catalyst, and the catalyst is processed into powder particles by mechanical grinding under anhydrous drying conditions before use.

[0077] The purified product is sampled and analyzed, specifically, the polymerization product (polycarbonate-polyether polyol and cyclic propylene carbonate and unreacted propylene oxide) is sampled and collected in a container, and the polymerization product logistics sample is characterized by nuclear magnetic hydrogen spectrum to calculate the ratio of polymer to cyclic small molecules in the crude product. After the polymer is purified, the nuclear magnetic hydrogen spectrum test is performed to calculate the ratio of polycarbonate chain segments to polyether chain segments on the polymer main chain. There are only two structures of polycarbonate chain segments and polyether chain segments on the polymer main chain, and the sum of the percentages of the two is 100%. The final product with a carbonate connection ratio of 50-65% is a qualified product, and 55-60% is a superior product.

[0078] With the help of 1 The amount of carbon dioxide introduced in the obtained polycarbonate polyether polyol (carbonate segment content) and the ratio of propylene carbonate (cyclic carbonate) to polycarbonate polyether polyol were determined by H-NMR (Bruker, DPX400, 400 MHz; pulse program zg30, waiting time d1:10 s, 64 scans). In each case, the sample was dissolved in deuterated chloroform. 1 The relevant resonant proton peaks in H-NMR (based on TMS=0ppm) are as follows: Among them, 5.0ppm and 4.2ppm belong to the proton peaks of the methine and methylene on the polycarbonate chain, 4.9ppm, 4.5ppm and 4.1ppm belong to the proton peaks of the methine and methylene in the five-membered ring carbonate, and 3.5-3.8ppm belong to the proton peak of the ether chain. The capital letter A plus a numerical subscript represents the integrated area of ​​the peak at a certain ppm on the nuclear magnetic resonance hydrogen spectrum, for example, A 5.0 represents the integrated area of ​​the peak at 5.0 ppm. 1 The integral area of ​​the H-NMR spectrum and its related proton peaks is used to calculate the carbonate chain ratio (molar ratio) F in the copolymerization reaction. CO2 and cyclic carbonate content mass fraction W PC , the amount of carbon dioxide embedded M CO2 , the specific calculation method is as follows: F CO2 =(A 5.0 +A 4.2 -2×A 4.6 ) / [(A 5.0 +A 4.2 -2×A 4.6 )+A 3.5 ]×100%; W PC =102×A 1.5 / [102×(A 5.0 +A 4.2 -2×A 4.6+A1.5)+58×A 3.5 ]×100%; M CO2 =44×F CO2 / [102×F CO2 +58×(1-F CO2 )]×100%.

[0079] The coefficient 44 is the molar mass of carbon dioxide, the coefficient 58 is the molar mass of PO (propylene oxide), and the coefficient 102 is the sum of the molar masses of carbon dioxide (molar mass 44 g / mol) and PO (molar mass 58 g / mol).

[0080] The formula for calculating the product conversion rate n is n=m×(1-M CO2 ) / (Mm×M CO2 ), wherein m is the mass of the product after removing propylene oxide, and M is the total sample mass containing propylene oxide.

[0081] The polymer number average molecular weight (Mn) and polymer molecular weight polydispersity index (PDI) are determined by gel permeation chromatography (GPC). The smaller the PDI of the final product, the better the quality. A PDI below 2 is a qualified product, and a PDI below 1.5 is a superior product.

[0082] The final product was tested for colorimetry. 5g of the test object was placed in a cuvette and the cuvette was placed in a dark environment to reduce the influence of the light source on the test and cause errors. The NR10QC colorimeter was used for the test, and the test data included three basic indicators: L*, a*, and b*.

[0083] The L* value represents the color brightness, L*=0 indicates black, and L*=100 indicates white.

[0084] The a* value represents the position between red / magenta and green, with negative a* values ​​indicating green and positive a* values ​​indicating magenta.

[0085] The b* value represents the position between yellow and blue, with a negative b* value indicating blue and a positive b* value indicating yellow.

[0086] The smaller the absolute values ​​of a* and b*, the better; the smaller they are, the closer they are to colorless.

[0087] The test results are shown in Table 1 below: Table 1

[0088] As can be seen from Table 1, the production process provided in the embodiment of the present application can fully react the raw materials at 80°C, with a conversion rate of 98%, and prepare high-quality polycarbonate polyether polyols, whose molecular weight distribution is narrower, distributed around 1.1; the carbonate chain segment ratio is higher, at more than 55%, that is, the carbon dioxide fixation rate is high. And the reaction time is very short, only 3 hours are needed to reach the established conversion rate.

[0089] Example 2 Compared with Example 1, other parameters and reaction materials of Example 2 have not been changed. The only change is that the gas intelligent control method as described above is adopted in Example 2. The experiment is shown in Table 2: Table 2

[0090] From the data of Example 2, it can be known that the gas intelligent control method described above can make the raw materials react fully. It should be noted that in Example 1, the reaction completion time to reach a predetermined conversion rate is 3 hours, while in Example 2, the reaction completion time is 2.5 hours, and the reaction efficiency is increased by 16.7%. The reaction products are tested and found to be equivalent in terms of molecular weight distribution and carbonate chain segment ratio, and the b* value of the chromaticity is reduced by more than 80%, which greatly reduces the generation of yellow and has better color quality.

[0091] Finally, it should be noted that the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The detailed description of the embodiments of the present invention is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Claims

1. A production process for low molecular weight bisphenol A type polycarbonate polyether polyol, characterized in that: include: Premixing step: mixing the epoxy compound, CO2, small molecule initiator and catalyst in a premixing unit to obtain a premix; Feeding step: feeding the premixture into the reaction zone; CO2 pressure maintenance step: after the feeding is completed, CO2 is introduced into the reaction zone until the pressure of the reaction zone reaches a first pressure value, and maintained at the first pressure value; Heating step: turning on the liquid circulation pump, adjusting the temperature of the medium in the jacket, so as to heat the reaction pipe to 75-85° C., and during the heating process of the reaction pipe, controlling the temperature difference between the medium in the jacket and the temperature of the reaction pipe to not exceed 3° C.; Circulation reaction step: after the temperature is raised, the liquid circulation pump is kept turned on, the temperature in the reaction pipeline is monitored, and when the temperature of the reaction pipeline is increased or decreased, the temperature of the medium in the jacket is correspondingly reduced or increased, and the reduced or increased temperature of the jacket medium is equal to the increased or reduced temperature of the reaction pipeline, so that the material temperature is maintained at 75-85°C, and the temperature in the reaction pipeline is monitored, and based on the temperature in the reaction pipeline, it is determined whether to supplement the reaction zone with purified CO2 gas of a second pressure value, the gas temperature of the purified CO2 gas is 58-62°C, the second pressure value is 4-8MPa, and the second pressure value is greater than the first pressure value; Wherein, determining whether to add purified CO2 gas to the reaction zone based on the temperature in the reaction pipe includes: obtaining a time queue of the temperature in the reaction pipe, and determining whether to add purified CO2 gas to the reaction zone based on the time queue of the temperature in the reaction pipe.

2. The production process of low molecular weight bisphenol A polycarbonate polyether polyol according to claim 1, characterized in that: Determining whether to add purified CO2 gas to the reaction zone based on the time sequence of the temperature in the reaction pipe comprises: Performing multi-dimensional time series correlation feature extraction on the time queue of the temperature in the reaction pipeline to obtain the time series correlation feature of the absolute value of the temperature in the reaction pipeline and the time series correlation feature of the temperature change in the reaction pipeline; Performing a dynamic-static joint analysis on the time series correlation characteristics of the absolute value of the temperature in the reaction pipeline and the time series correlation characteristics of the temperature change in the reaction pipeline to obtain a dynamic-static time series joint characteristic of the reaction temperature; Based on the dynamic-static timing joint characteristics of the reaction temperature, it is determined whether to add purified CO2 gas to the reaction zone.

3. The production process of low molecular weight bisphenol A type polycarbonate polyether polyol according to claim 2, characterized in that: The multi-dimensional time series correlation feature extraction is performed on the time queue of the temperature in the reaction pipeline to obtain the time series correlation feature of the absolute value of the temperature in the reaction pipeline and the time series correlation feature of the temperature change in the reaction pipeline, including: Calculate the temperature gradient value of the temperature in the reaction pipe at each time point in the time queue of the temperature in the reaction pipe to obtain a time queue of the temperature gradient value; Extracting the time series correlation feature of the absolute value of the temperature in the reaction pipeline from the time queue of the temperature in the reaction pipeline; The time series correlation characteristics of the temperature variation in the reaction pipeline are extracted from the time queue of the temperature gradient value.

4. The production process of low molecular weight bisphenol A type polycarbonate polyether polyol according to claim 3, characterized in that: Extracting the time series correlation characteristics of the absolute value of the temperature in the reaction pipeline from the time queue of the temperature in the reaction pipeline includes: The time queue of the temperature in the reaction pipeline is input into a time series feature extractor based on the RNN model to obtain the time series correlation feature of the absolute value of the temperature in the reaction pipeline.

5. The production process of low molecular weight bisphenol A polycarbonate polyether polyol according to claim 4, characterized in that: Performing a dynamic-static joint analysis on the time series correlation characteristics of the absolute temperature in the reaction pipeline and the time series correlation characteristics of the temperature change in the reaction pipeline to obtain a dynamic-static time series joint characteristic of the reaction temperature, including: Performing fine-grained feature timing decoupling on the time series correlation features of the absolute temperature in the reaction pipeline and the time series correlation features of the temperature variation in the reaction pipeline to obtain a set of local time series correlation features of the absolute temperature in the reaction pipeline and a set of local time series correlation features of the temperature variation in the reaction pipeline; Performing optimal feature subcomponent pairing screening on the set of local time series correlation features of the absolute value of temperature in the reaction pipeline and the set of local time series correlation features of the temperature variation in the reaction pipeline to obtain an optimal {local time series correlation features of the absolute value of temperature in the reaction pipeline, local time series correlation features of the temperature variation in the reaction pipeline} pairing set; The optimal sub-component feature pairing interactive fusion and feature cascade are performed on the set of the optimal {local time series correlation features of the absolute value of temperature in the reaction pipeline, local time series correlation features of the temperature change in the reaction pipeline} pairs to obtain the dynamic-static time series joint features of the reaction temperature.

6. The production process of low molecular weight bisphenol A polycarbonate polyether polyol according to claim 5, characterized in that: The set of local time series correlation features of the absolute value of temperature in the reaction pipeline and the set of local time series correlation features of the temperature change in the reaction pipeline are screened for the best feature subcomponent pairing to obtain the best {local time series correlation features of the absolute value of temperature in the reaction pipeline, local time series correlation features of the temperature change in the reaction pipeline} pairing set, including: For each local time series correlation feature of the absolute quantity of temperature in the reaction pipe in the set of local time series correlation features of the absolute quantity of temperature in the reaction pipe, calculate the hyperbolic space distance measurement factor between it and each local time series correlation feature of the change quantity of temperature in the reaction pipe in the set of local time series correlation features of the change quantity of temperature in the reaction pipe, and select the local time series correlation feature of the change quantity of temperature in the reaction pipe corresponding to the smallest hyperbolic space distance measurement factor as the pairing object to obtain the optimal {local time series correlation feature of the absolute quantity of temperature in the reaction pipe, local time series correlation feature of the change quantity of temperature in the reaction pipe} pairing set.

7. The production process of low molecular weight bisphenol A polycarbonate polyether polyol according to claim 6, characterized in that: The optimal sub-component feature pairing interactive fusion and feature cascade are performed on the set of the optimal {local time series correlation feature of the absolute value of temperature in the reaction pipeline, local time series correlation feature of the temperature change in the reaction pipeline} pairing to obtain the dynamic-static time series joint feature of the reaction temperature, including: Performing feature multi-dimensional interaction on each optimal {local time series correlation feature of absolute value of temperature in reaction pipeline, local time series correlation feature of temperature variation in reaction pipeline} pair in the set of the optimal {local time series correlation feature of absolute value of temperature in reaction pipeline, local time series correlation feature of temperature variation in reaction pipeline} pair to obtain a set of dynamic-static time series multi-dimensional interaction features of reaction temperature; The set of multi-dimensional interactive features of the reaction temperature dynamic-static time series is cascaded to obtain the reaction temperature dynamic-static time series joint feature.

8. The production process of low molecular weight bisphenol A polycarbonate polyether polyol according to claim 7, characterized in that: For each optimal {local time series correlation feature of the absolute value of temperature in the reaction pipeline, local time series correlation feature of the change value of temperature in the reaction pipeline} pair in the set of the optimal {local time series correlation feature of the absolute value of temperature in the reaction pipeline, local time series correlation feature of the change value of temperature in the reaction pipeline} pair, feature multi-dimensional interaction is performed to obtain a set of dynamic-static time series multi-dimensional interaction features of reaction temperature, including: Respectively calculate the position point addition, position point subtraction and position point multiplication between the local time series correlation feature of the absolute value of temperature in the reaction pipeline and the local time series correlation feature of the change in temperature in the reaction pipeline in the optimal {local time series correlation feature of the absolute value of temperature in the reaction pipeline, local time series correlation feature of the change in temperature in the reaction pipeline} pair to obtain a first reaction temperature dynamic-static time series interaction feature, a second reaction temperature dynamic-static time series interaction feature and a third reaction temperature dynamic-static time series interaction feature; The position-weighted sum of the first reaction temperature dynamic-static time series interaction feature, the second reaction temperature dynamic-static time series interaction feature, and the third reaction temperature dynamic-static time series interaction feature is calculated to obtain the reaction temperature dynamic-static time series multi-dimensional interaction feature.

9. The production process of low molecular weight bisphenol A polycarbonate polyether polyol according to claim 8, characterized in that: Determining whether to add purified CO2 gas to the reaction zone based on the dynamic-static timing joint characteristics of the reaction temperature includes: The dynamic-static timing joint characteristics of the reaction temperature are input into a classifier-based valve controller to obtain a control instruction, wherein the control instruction is used to indicate whether to supplement the reaction zone with purified CO2 gas.

10. The production process of low molecular weight bisphenol A polycarbonate polyether polyol according to claim 1, characterized in that: A gas-liquid mixing heat conduction unit and a solid-liquid mixing unit are arranged in the reaction pipeline, and the gas-liquid mixing heat conduction unit includes a dispersion sheet and a gas-liquid mass transfer zone, wherein the gas-liquid mixing heat conduction unit is used to redissolve CO2 in the mixed material with a low CO2 content in the reaction zone, and heat exchange is performed in the gas-liquid mixing heat conduction unit at the same time; the solid-liquid mixing unit is used to disperse the solid catalyst and disperse the reaction heat.

11. A gas intelligent control method for the production process of low molecular weight bisphenol A polycarbonate polyether polyol according to claim 1, characterized in that: include: A time sequence for obtaining the temperature in the reaction pipe; Performing multi-dimensional time series correlation feature extraction on the time queue of the temperature in the reaction pipeline to obtain the time series correlation feature of the absolute value of the temperature in the reaction pipeline and the time series correlation feature of the temperature change in the reaction pipeline; Performing a dynamic-static joint analysis on the time series correlation characteristics of the absolute value of the temperature in the reaction pipeline and the time series correlation characteristics of the temperature change in the reaction pipeline to obtain a dynamic-static time series joint characteristic of the reaction temperature; Based on the dynamic-static timing joint characteristics of the reaction temperature, it is determined whether to add purified CO2 gas to the reaction zone.

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