Abstract

Dynamic optimization is an important research topic in chemical process control. A dynamic optimization method with good performance can reduce energy consumption and prompt production efficiency. However, the method of solving the problem is complicated in the establishment of the model, and the process of solving the optimal value has a certain degree of difficulty. Based on this, we proposed a non-fixed points discrete method of an enhanced beetle antennae optimization algorithm (EBSO) to solve this kind of problem. Firstly, we converted individual beetles into groups of beetles to search for the best and increase the diversity of the population. Secondly, we introduced a balanced direction strategy, which explored extreme values in new directions before the beetles updated their positions. Finally, a spiral flight mechanism was introduced to change the situation of the beetles flying straight toward the tentacles to prevent the traditional algorithm from easily falling into a certain local range and not being able to jump out. We applied the enhanced algorithm to four classic chemical problems. Meanwhile, we changed the equal time division method or unequal time division method commonly used to solve chemical dynamic optimization problems, and proposed a new interval distribution method—the non-fixed points discrete method, which can more accurately represent the optimal control trajectory. The comparison and analysis of the simulation test results with other algorithms for solving chemical dynamic optimization problems show that the EBSO algorithm has good performance to a certain extent, which further proves the effectiveness of the EBSO algorithm and has a better optimization ability.

Highlights

  • Introduction iationsChemical process control is a dynamic process in which state variables change over time and space dimensions are adjusted

  • The dynamic optimization problem (DOP) means that the optimal control range gradually tends to the current dynamic operating volume under the condition of safety and constraints [13]

  • These results suggest that the EBSO algorithm has good solution results in low segment numbers to a large number of random segments, which further proves the effectiveness of the EBSO algorithm proposed in this paper

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Summary

Description of Dynamic Optimization Problem

The dynamic optimization problem (DOP) means that the optimal control range gradually tends to the current dynamic operating volume under the condition of safety and constraints [13]. The mathematical model of the dynamic optimization problem is described as a differential–. The mathematical model of the dynamic problem describes the goals as follows: MinJ (u) = φ[ x (t f )] +. Solving the dynamic optimization problem requires finding the optimal control strategy u(t) to minimize the performance index J obtained by the process under the condition of satisfying the constraints

Non-Fixed Points Discrete Division Method
Performance Analysis
Calculate
Enhanced
Beetle Swarm
Balanced Direction Strategy
Introducing the Spiral Flight Mechanism
Initialize
Update
Benchmark Functions
Parameter Settings
Statistical Result Comparison
15. Iterative
Application
Experimental Process Analysis
Case 1
Case 2
C A and the safety concentrations
The optimal temperature control
Comparison methods batchreactor reactor
3: Tubular
31. Optimal
Case 4
34. Optimal
38. Statistical
Conclusions

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