Synchronous method is used to establish an integrated model for batch process production scheduling and control. In the scheduling section, a production scheduling model is established based on the State Equipment Network (SEN) and the unit?specific event?based continuous time modeling method; the integrated model of scheduling and control belongs to a mixed integer dynamic optimization problem, and solving it requires a large amount of complex computation, in order to alleviate the burden of online computing, Explicit Model Predictive Control (EMPC) is utilized for offline solving; the MPT toolbox is used to solve the dynamic problem of EMPC; introducing binary variables, converting the obtained explicit control solution into explicit linear constraints, and adding them to the common constraint objective in the scheduling model; through case analysis, the optimization results were compared and analyzed with the pure scheduling model, and the economic feasibility of the integrated model is verified.

For the MIMO nonlinear systems, a multivariable ORVFL neural network adaptive predictive control algorithm based on Improved Sparrow Search Algorithm was proposed in this paper. The algorithm uses the ORVFL network to approximate the nonlinear system model, and applies to the multi?step prediction of the system process. In order to improve the performance of the Sparrow Search Algorithm, the algorithm is used to optimize the system performance index online and solve the optimal control law of each sampling period. The results show that the algorithm has good control performance and good anti?model mismatch ability.

A novel distributed model predictive control (DMPC) approach based on immune algorithm (IA) to find out the optimal system decomposition structure is proposed. The IA is used to solve decomposition problems for input clustering decomposition (ICD) and input?output pairing decomposition (IOPD), which can minimize the impact of input?output coupling between systems, and then DMPC algorithm is used to control the decomposed system. This approach effectively reduces the coupling between subsystems, and reduces the communication load of the system. Finally, a heavy oil fractionation chemical process is simulated and compared with the centralized MPC simulation results to verify the effectiveness of the algorithm.