99 results about "Process industry" patented technology

The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a manufacturing quality prediction method and system based on a multi-mode sequential network and application, and the method comprises the steps: carrying out the preprocessing of the sequential data of a process manufacturing production line, obtaining a sample set, and carrying out the sequential division into a training set, a verification set and a test set; on the basis of the sample set, key features are enhanced through a frequency domain enhanced channel attention mechanism, a multi-period mode of a time sequence dependence and period sensing module is captured in combination with a multi-layer expansion convolutional network structure, and a multi-mode time sequence network model is constructed; and sequentially carrying out training set training, verification set parameter adjustment optimization and test set performance verification on the multi-modal sequential network model, and outputting a prediction result. According to the method, the deep dynamic association among the multivariable time series data can be mined, the accuracy and robustness of manufacturing quality prediction are improved, and an efficient and reliable technical scheme and an implementation path are provided for process industry quality control and intelligent optimization.

The invention provides a dynamic risk management and control system and risk assessment early warning method for a process industry, and the system comprises a micro-service data design module, an engine configuration module, an AP I interaction module, a data configuration module, a task generation module, and a simulation module. Simulation, early warning and strategy generation of the whole production process of the process industry are realized by adopting a micro-service framework and a container dynamic arrangement technology, key scenes of the whole production process of the process industry are covered, specific scene application verification can be realized based on existing industrial control, safety, environmental protection, equipment management and the like, and data of various links and the like in the production process are acquired, so that the production efficiency is improved. The platform function and the use effect thereof are comprehensively evaluated in a specific scene, abnormal conditions are quickly identified and positioned, improvement measures are formulated and implemented, self-learning and self-adaptive capabilities are realized, early warning and management and control strategies can be flexibly adjusted, and the method has flexibility.

The invention provides a problem semantic enhancement method and device oriented to a process industry large model. The method comprises the following steps: acquiring an original problem of a user, and rewriting the original problem of the user by adopting a pre-training language model based on a natural language processing technology to obtain a rewritten problem of the user; a standardized term dictionary based on the field of the process industry is constructed, a user rewriting problem is standardized according to the standardized term dictionary to obtain a standard user problem, and based on similarity comparison of a standard problem library and word vectors, a mask language model is adopted to predict a near-synonym expression of the standard user problem, so that the user rewriting problem is obtained. Obtaining a plurality of candidate questions corresponding to the standard user question, and determining an optimal candidate question in the plurality of candidate questions; and inputting the optimal candidate question into a large model question-answering system based on the process industry, and generating a question reply corresponding to the optimal candidate question. The problem that in the prior art, in a process industrial scene, a result obtained by querying a large model according to a user question is low in accuracy is solved.

The invention provides a prediction control method and device adaptive to a process industry model and electronic equipment. The method comprises the following steps: acquiring a historical controlled quantity sequence, a historical controlled quantity sequence and a future controlled quantity sequence of the chemical device; a unit step response prediction model is constructed, the historical controlled quantity sequence and the historical controlled quantity sequence are input into the unit step response prediction model, unit step response based on the controlled quantity and the controlled quantity is obtained, and the unit step response prediction model comprises a coding layer, an attention layer, a decoding layer and a smoothing layer; constructing a controlled quantity prediction model, and inputting the historical controlled quantity sequence, the historical controlled quantity sequence, the future controlled quantity sequence and the unit step response into the controlled quantity prediction model to obtain a future controlled quantity sequence corresponding to the future controlled quantity sequence; and the future controlled quantity sequence is controlled to be within a set range by adjusting the future controlled quantity sequence. The problem that in the prior art, prediction of nonlinear variables in the field of process industrial control is low in accuracy is solved.

The invention provides an energy efficiency evaluation and intelligent diagnosis method for a process industrial production system, and the method comprises the steps: determining an energy efficiency evaluation index, building an energy balance equation, and calculating the heat efficiency, efficiency and energy consumption of each equipment, production process and system according to the equipment characteristics, process matching and overall production process in industrial production; an industrial production process database is constructed, and data preprocessing is carried out; based on the industrial production process database, quantitatively evaluating the influence of each parameter on heat efficiency, efficiency and energy consumption by adopting a grey relational analysis method, and taking the parameter with relatively high influence as a key parameter; linear relations between the key parameters and the heat efficiency, efficiency and energy consumption are analyzed by constructing a multiple regression model; carrying out hierarchical division on key parameters, screening a reference working condition based on similarity analysis K-Means clustering, determining and comparing an optimal working condition with an actual working condition, implementing intelligent diagnosis, taking energy consumption as a core evaluation target, determining an optimal production mode, and carrying out auxiliary decision making.

The invention relates to the technical field of digital twinning, and discloses a process industry digital twinning platform system, which comprises a process mechanism modeling module used for constructing a multi-scale first principle model system according to physical and chemical laws of the process industry; the dynamic coupling engine is used for collecting process data in real time and carrying out coupling solution by combining the first principle model system and the equipment operation state model; the closed-loop optimization control interface is used for carrying out two-way communication with the distributed control system, generating an optimization set value based on a solving result and executing control; and the visual diagnosis module is used for carrying out multi-dimensional analysis, fault root cause tracing and early warning response on process parameter deviation. First principle equations of reaction kinetics, heat transfer theory and the like are directly embedded into the virtual model through the process mechanism modeling module, so that the model has physical and chemical interpretation capability on complex chemical reactions and energy transfer, and high precision is still kept under complex working conditions.

The invention provides a self-adaptive deep learning prediction control method and device suitable for the process industry. Comprising the following steps: constructing an all-condition dynamic model of a target control variable based on a deep learning technology according to historical control information of the process industry, and performing segmentation processing on a change gain curve of the all-condition dynamic model based on a self-adaptive segmentation algorithm of a threshold value to obtain a segmented linear gain model; a piecewise linear variable constraint of the piecewise linear gain model is determined, and an optimization target of a target control variable is determined based on the piecewise linear variable constraint; and obtaining a step response sequence of the target control variable at the current working condition point based on the piecewise linear gain model, and predicting a future control execution sequence of the target control variable according to the step response sequence and the optimization target. The problem that an existing predictive control method generally constructs a mapping relation between a control variable and a controlled variable based on a linear model, the nonlinear characteristic of the control variable cannot be accurately reflected, and then the predictive control accuracy is low is solved.

The invention discloses a multi-model fusion process quality prediction method based on ensemble learning, and relates to the technical field of process industry quality prediction. The method comprises three stages of data preprocessing, model construction and model fusion. In the data preprocessing stage, cleaning, normalization and feature screening are performed on multi-process production data; in the model construction stage, an overall prediction model and a segmented prediction model are constructed respectively, the overall prediction model adopts RF, LightGBM and KNN algorithms, and the segmented prediction model adopts an LSTM-KAN combined neural network; in the model fusion stage, prediction results of the two models are fused through an XGBoost ensemble learning algorithm, and advantage complementation is achieved. According to the method, the quality prediction problem caused by high process coupling degree and remarkable nonlinear relation in complex process manufacturing is effectively solved, the prediction precision and generalization performance are improved, and the method is suitable for a multi-process complex production scene.

The invention relates to the technical field of intelligent fault prediction, and provides a multi-dimensional time sequence anomaly detection method for the process industry, which aims at historical and real-time multi-source data of the process industry, performs adaptive feature extraction to obtain better features through a feature selection module fusing mRMR and a self-attention mechanism, and improves the detection accuracy. Multivariable time series data double-fusion prediction calculation is completed through a sequence prediction model, the incidence relation of data at different time points and the incidence relation of different features at the same time are mined, and after cross entropy loss and optimizer training optimization are carried out, abnormal state judgment is finally achieved in combination with a dynamic threshold value. The invention further discloses a system used for the method, the method and the system can mine mechanism information implied by equipment from historical multi-source data in the process industry, time and features are subjected to relevance fusion respectively, and therefore the method and the system can be more suitable for the unsteady state and strong coupling conditions of the process industry; and particularly, a remarkable effect is achieved in a slag grinding system.

The invention discloses a smelting method of cold direct reduced iron in a Consteel electric furnace, belonging to the technical field of electric furnace steelmaking. The smelting method comprises the following steps: at the beginning of smelting, low-grade power supply is adopted, and at the same time of power-on, oxygen lances and carbon lances on the furnace wall are used to blow oxygen and spray carbon to produce foamed slag for submerged arc. After 1-3 minutes, scrap steel is continuously added by the Consteel system, and cold direct reduced iron is added through the fifth hole of the electric furnace. The molten steel in the furnace is heated by the electric arc, and the molten steel melts the metal materials in the furnace; during the whole smelting process, lime and light burned dolomite are added in batches. The addition amount of lime is 30-50 kg / t of steel, and the addition amount of light burned dolomite is 25-30 kg / t of steel. The invention helps to maintain the whole-process flat bath smelting, reduce the power consumption of electric furnace smelting, and reduce the smelting cost; it provides a technical solution for a short-process demonstration plant for the efficient use of direct reduced iron in an electric furnace, filling the domestic technical research gap in the short-process industry of electric furnaces.

The embodiment of the invention provides a process industry real-time monitoring and early warning system and method and a storage medium, and the system comprises a data collection module which is used for receiving the safety instrument data of each device and the sensing data of each sensor in the process industry in real time; the verification module is used for calling an SIL level verification model to perform online verification on the data according to the safety instrument data, and determining risk information of each piece of equipment; the anomaly detection module is used for determining anomaly data based on an anomaly detection model according to the sensing type data and the risk information; and the early warning module is used for generating early warning information according to the abnormal data and the risk information. Through cooperative work of all the modules, safety and reliability in the industrial production process can be remarkably improved, potential problems can be warned in advance, non-planned downtime and maintenance cost are reduced, meanwhile, the service life of equipment is prolonged, and therefore production efficiency is improved.

The invention relates to the technical field of process industry process modeling and data-driven prediction, and discloses a process industry key index prediction method and device based on time-delay distribution learning. The method and the device are used for solving the problems of unreasonable historical information alignment and insufficient key index prediction precision and stability caused by difficulty in measurement of time-delay distribution attributes in process industry multivariable data. According to the method, a current window, a candidate lag historical window and a future target window are constructed by adopting a sliding window, time-lag weight distribution is learned on a candidate lag axis based on a state gating cross attention mechanism, time-lag distribution learning and a future window prediction task are jointly optimized through a time-lag modeling auto-encoder, and finally, time-lag prediction is performed on the candidate lag historical window and the future target window. And outputting a key index prediction value under the guidance of time-delay distribution. The method is suitable for online prediction of key indexes such as quality, yield and energy consumption of the process industry, and can be used for process monitoring and optimization control.

The application provides a process industry soft measurement method based on feature extraction and incremental learning. The method introduces an end-to-end neural network soft measurement model composed of multiple parallel time series feature extraction self-encoder networks and a regressor network connected in front and back, extracts time series features from multi-process variable time series data, and uses the time series features for performance index soft measurement modeling. In addition, based on the time series features extracted by the model, the method proposes an incremental learning method based on time series features. The soft measurement method provided by the application can effectively and accurately perform soft measurement on some performance indexes that are difficult to directly measure or have high measurement cost in the process industry, and through the incremental learning method, the soft measurement model can long-term maintain good soft measurement performance in the industrial data stream with strong real-time performance, thereby providing reference and guidance for monitoring, measurement, optimization and control of various parameters in the process industry production process.

The invention discloses a complex process industry-oriented multi-level progressive parameter optimization method, which comprises the following steps of: analyzing the correlation between M process parameters and a quality index one by one to determine the importance degree of each process parameter to the quality index; sorting the importance degrees of the quality indexes in a descending order according to the process parameters to obtain the process parameters sorted according to the importance degrees; according to the sorted technological parameters, a technological parameter layering method based on correlation analysis is constructed, hierarchical division of the technological parameters is achieved, and a multi-level technological parameter combination is obtained through C times of division; modeling optimization is carried out in sequence from the Cth layer of technological parameter combination to the first layer of technological parameter combination, and the optimal solution of the first layer of technological parameter combination to the optimal solution of the Cth layer of technological parameter combination are obtained. According to the method, the problem of optimization difficulty caused by low modeling prediction precision and various process parameters in complex process manufacturing is effectively solved, and the method is of great significance in guaranteeing the process quality and improving the processing efficiency for process manufacturing enterprises.

The invention discloses a whole-membrane-process water treatment operation system for a process industry. Comprising a raw water pump, a self-cleaning filter, an ultrafiltration device, an ultrafiltration water tank, an ultrafiltration water pump, a security filter, a first-stage water feeding pump, a first-stage reverse osmosis device, a water pump, a security filter, a second-stage water feeding pump, a second-stage reverse osmosis device, a fresh water tank, an EDI water feeding pump, a security filter and an EDI device which are sequentially connected through pipelines, the system is composed of an intelligent operation system of an ultrafiltration membrane system, an intelligent operation system of a reverse osmosis membrane system, an intelligent operation system of an EDI membrane system and dosing and cleaning auxiliary equipment. The system not only collects and stores production operation data in real time, visually displays the operation state of the membrane treatment equipment and gives an alarm in time, but also realizes accurate dosing control and energy-saving optimized operation by automatically diagnosing the membrane-method water treatment system equipment, so that the safety and economical efficiency of whole-process industrial water production are realized.

A method and system for attack and defense based on dynamic simulation in chemical engineering includes: constructing an adversarial training environment for chemical equipment and processes using dynamic simulation units; setting attack and defense training parameters through a task configuration unit and generating corresponding fault interference test cases from a fault interference mode library; the attacker selecting a fault interference test case, adjusting the fault interference parameters using an attack strategy, and injecting it into the adversarial training environment; the defender receiving alarm information from the fault interference test cases and performing handling operations on them; and an evaluation unit performing real-time evaluation and display. This invention, through the deep integration of dynamic simulation technology and attack and defense mechanisms, upgrades chemical skills training from static procedure drills to dynamic engineering practice, providing core technical support for the cultivation of highly skilled personnel in the process industry, and has significant economic and social value.

The application provides an industrial heat exchange process operation optimization control physical experiment device and method, and relates to the technical field of automatic control of process industry. The device comprises: a controlled object unit configured to perform operation optimization control experiment; a process control unit configured to detect process parameters of the controlled object in real time, receive a set value instruction from the operation control unit, and adjust a control instruction to realize basic loop control of the controlled object; and an operation control unit configured to read and record industrial heat exchange process data from the process control unit, perform a specific optimization algorithm calculation according to an optimization target required by the experiment, and adjust the set value of the basic loop control of the controlled object to realize optimization of the operation index. The method is based on the experiment device, three basic loop controllers are constructed, the operation index, decision variable and control problem of the operation optimization of the controlled object are defined, and experiment research for realizing optimization of the operation index is performed.

The invention discloses a key factor identification method for influencing process industrial load participation in demand response and computer equipment thereof, and relates to the field of power system scheduling and control. Firstly, a state task network is utilized to describe a production link coupling relation, and a maximum total system profit is taken as an optimization target; and constructing an energy optimization scheduling model for the general industrial process. Secondly, common influence factors in the production process are extracted for a power grid and process industrial production interaction scene; the method comprises the following steps of: performing preliminary rapid screening on a large number of influence factors on the basis of a Morris screening method, and finally quantifying global sensitivity coefficients of the screened influence factors on three types of core indexes, namely economical efficiency, productivity and energy efficiency on the basis of a Sobol 'global sensitivity analysis method, so as to identify key factors which influence the participation of the process industrial load in demand response.

The present application belongs to the technical field of liquid overpressure energy recycling in process industry, and discloses a self-driven double-rotating disc type energy recycling device, which comprises a first cylinder and a second cylinder which are symmetrical and connected in the same structure, and the internal space of the first cylinder and the second cylinder is provided with a special-shaped shaft, a stator, a first rotating disc, a second rotating disc, a first sleeve and a second sleeve which are coaxially arranged; the first rotating disc and the first sleeve are located at one end of the stator, and the second rotating disc and the second sleeve are located at the other end of the stator; the first sleeve and the second sleeve are fixedly connected with the stator through bolts, and the first rotating disc and the second rotating disc are coaxially and staggeredly connected at both ends of the stator through the special-shaped shaft. Through the technical measures of circumferential inflow impact driving double-rotating disc coaxial rotation movement and pressure exchange of high and low pressure fluids in the stator channel, the present application realizes independent operation of rotary switching and pressure exchange, better dynamic balance performance and other effects, which is beneficial to improve the operation stability and efficiency of the device, and is easy to realize engineering amplification of the treatment capacity.

The invention relates to a working condition automatic clustering method, system and device for process industry time series data and a medium, and the method comprises the steps: carrying out the sliding window segmentation of preprocessed multivariable time series data, and obtaining subsequence data; based on behavior pattern information of the sub-sequence data, calling a corresponding multi-dimensional feature extraction strategy to perform feature extraction and fusion, and generating a global feature vector; performing clustering analysis on the global feature vector set on the basis of an optimal clustering number obtained by analyzing, evaluating and optimizing the contour coefficient and the in-cluster consistency to obtain a cluster label of data working condition classification; and mapping the cluster labels back to a time axis of the multivariable time series data, and carrying out visualization processing of typical working condition pattern recognition on the obtained working condition label sequence data to obtain an analysis result representing the multi-working condition characteristics of the flow industrial process. The method can effectively capture time sequence dynamic characteristics, has good anti-noise capability and high calculation efficiency, and is highly suitable for operation state identification and analysis in the process industry.

In an industrial plant, various equipment are used to handle processing of raw materials. Considering complexities involved in the processes and the equipment, constant monitoring is required to obtain desired results. The disclosure herein generally relates to industrial process and equipment monitoring, and, more particularly, to data analysis for Just In Time (JIT) characterization of raw materials in any process industry. The system collects real-time plant data among other inputs, and performs characterization of raw materials being used in the plant. The characterization involves categorizing the raw materials into different classes. The class information is further used to predict performance of the industrial plant, and in turn to generate recommendations for optimization of the industrial plant.

This invention relates to the technical field of product quality prediction in process industries, specifically to a method for constructing a tread product quality prediction model in the tire manufacturing industry. The method includes the following steps: S1: Collecting and organizing multi-source heterogeneous tread production process data and product quality inspection data to establish a structured dataset with a unified format; S2: Using the isolated forest algorithm to detect and remove outliers in the data; S3: Completing sensor time calibration alignment based on the dynamic characteristics of the production process and a numerical sensor time calibration method using timestamps; S4: Extracting features from the tread time-series data; S5: Decomposing the feature set and feeding it into an improved LSINet model to establish a tread quality prediction model, obtaining the final tread quality prediction result. This invention provides a method for analyzing tread quality in the tire manufacturing industry, helping companies trace quality problems of non-conforming products and improve tread product quality and production efficiency.

This invention relates to the fields of industrial control and intelligent manufacturing, and particularly to an adaptive method, system, device, and medium for control parameters in variable-distribution process industries. The method includes: extracting features from process variable data, combining their distribution offset with control performance indicators for collaborative judgment, and outputting a trigger signal; upon receiving the trigger signal or system startup, parsing historical operating data to construct a behavioral proxy model and a historical benchmark distribution, and determining initial parameters by optimizing the consistency measure between the predicted behavioral distribution under the parameters to be optimized and the historical benchmark distribution; shaping the process variable data and inputting it into a strategy model to perform component extraction and inference to adjust the control parameters and obtain the parameters to be activated; evaluating the parameters to be activated, and if they exceed limits, reverting the parameters to the initial parameters, and adjusting the strategy model based on system response data. This invention improves the performance stability and adaptability of process industry control systems under complex and variable operating conditions.

The present invention discloses a pre-training method for multivariate time series prediction tasks in process industries, which relates to the field of time prediction technology, and specifically includes S1: data division and pre-processing; S2: working condition identification and feature extraction; S3: weight matrix calculation; S4: hidden layer representation generation; S5: pre-training model output; S6: pre-training model training; S7: pre-training model output. The method proposed in the present invention is applied to an attention-based graph convolutional neural network model (ASTGCN). Through verification on an industrial data set of a distillation section provided by a chemical plant, it is found that the root mean square error of the pre-trained model that takes working condition identification into consideration is reduced by about 15% compared to the model that is not pre-trained. In addition, compared to the pre-trained model that does not take working condition identification into consideration, its root mean square error is reduced by about 5%.

The application discloses a kind of process industry production control method and system based on waste heat recovery optimization, it is related to industrial production control field, the method comprises: according to the thermodynamic measurement data of each process stream in production system, under the minimum heat transfer temperature difference constraint, constructs multiple temperature intervals;According to the thermodynamic measurement data of each process stream, cascade calculation is carried out to multiple temperature intervals, to determine the upper limit of available waste heat;With the upper limit of available waste heat as the energy constraint of production system, with the maximum output flow of primary steam, secondary steam and heat medium water of production system as target, successively constructs and solves the optimization model of the output flow of primary steam, secondary steam and heat medium water, obtains the optimized output flow of primary steam, secondary steam and heat medium water, to control production system, to realize waste heat recovery of production system.By implementing the present application, the calculation efficiency of solving waste heat recovery and reuse in the production system can be improved.

The application discloses a kind of multi-level progressive parameter optimization methods for complex process industry, comprising: the correlation between M process parameters and quality indicators is analyzed one by one to determine the importance of each process parameter to quality indicators;According to the importance of process parameters to quality indicators, descending order is sorted, and the process parameters after importance degree sorting are obtained;According to the process parameters after sorting, the process parameter layering method based on correlation analysis is constructed, the hierarchical division of process parameters is realized, and the multi-level process parameter combination is obtained by C times division;Modeling optimization is sequentially carried out from the Cth layer process parameter combination to the 1st layer process parameter combination in order, and the 1st layer process parameter combination optimal solution to the Cth layer process parameter combination optimal solution is obtained.The application effectively solves the problems of low modeling prediction accuracy and optimization difficulty caused by numerous process parameters in complex process manufacturing, which is of great significance for process manufacturing enterprises to ensure process quality and improve processing efficiency.

The application discloses a multi-agent manufacturing process optimization method based on a multi-objective particle swarm algorithm, and comprises the following steps: for a manufacturing process of a process industry, a manufacturing process optimization model based on multi-agents is constructed, including a two-layer structure, wherein the upper layer structure is a total control agent and a pool agent used for storing algorithms and data information, and the lower layer structure is a raw material agent, a device agent, a management agent and a waste agent; each intelligent agent can interact with other agents based on a communication module of the intelligent agent; the agent is used as a particle in the multi-objective particle swarm algorithm, the agent population evolution ability is given, a corresponding data model is established for the multi-agent-based manufacturing process optimization model, the multi-objective particle swarm algorithm is used for solving, and an effective solution set is obtained. The cost after the optimization is lower than the actual cost, so the non-inferior solution set after the optimization can be used as a reference in actual production, so that the process operation manufacturing is improved in low consumption, high yield, high quality and cost reduction.

The application provides a process industry quality intelligent tracing and process optimization method and system, which is applied to the technical field of industrial production quality control and process optimization, realizes rapid diagnosis of quality abnormalities and accurate positioning of process causes by constructing a full-link automatic tracing model from external feedback data to production process parameters. The traditional quality control method is converted into a structured analysis path by using a digital lean rule engine, completely eliminating the dependence on manual experience. At the same time, by introducing a self-learning process strategy library and a closed-loop feedback mechanism, the system can automatically update and optimize the strategy according to the implementation effect of the scheme, realize the transition of quality control from post-processing to prediction and prevention, and greatly improve the production quality management efficiency and process robustness of the process industry.

The application relates to the technical field of operation and maintenance management, and discloses an operation and maintenance management method and system for process industrial equipment, the method comprising the following steps: coupling multi-modal features of time sequence operation parameters and field state information of the process industrial equipment to obtain a fusion feature set of the process industrial equipment; quantifying fluctuation trends of the fusion feature set to obtain a degradation feature index of the process industrial equipment; performing evolution evaluation on the degradation feature index to obtain a health quantitative value of the process industrial equipment; constructing operation and maintenance decision boundary conditions of the process industrial equipment; performing collaborative mapping on the fusion feature set and the health quantitative value to obtain an optimized operation and maintenance strategy of the process industrial equipment; and associating and integrating the operation and maintenance decision boundary conditions and the optimized operation and maintenance strategy to obtain an operation and maintenance management report of the process industrial equipment; and the application can improve the operation and maintenance management efficiency of the process industrial equipment.

The present application relates to the field of chemical process simulation, and particularly relates to a simultaneous equation simulation block solving method for process industry. The method comprises the following steps: extracting equations in an activated state and variables in an unfixed state from a chemical process model to form an original equation and variable set to be solved; converting the equation and variable set into a graph data structure reflecting system dependency according to a preset block strategy; using a strongly connected component identification algorithm to traverse the graph data structure to obtain a strongly connected component set, each strongly connected component in the strongly connected component set corresponding to a sub-equation block in the process equation; determining a solving order of the sub-equation blocks according to the dependency of the sub-equation blocks; solving each sub-equation block according to the solving order of the sub-equation blocks; and collecting the solving results of each sub-equation block to obtain a global solution of the complete chemical process model.