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An Integrated Biomimetic Control Strategy with Multi-agent Optimization for Nonlinear Chemical Processes ?

机译：具有多种子体化学过程的多种子体优化的综合仿生控制策略 ？

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摘要

In this paper, a framework is proposed for integrating a Biologically-Inspired Optimal Control Strategy (BIO-CS) with Multi-Agent Optimization (MAO) algorithms for process systems engineering applications. In this framework, the BIO-CS employs gradient-based optimal control solvers in an intelligent manner to simultaneously control multiple outputs of the process at their desired setpoints. Also, the MAO uses the capabilities of nonlinear heuristic-based optimization techniques such as Efficient Ant Colony Optimization (EACO), Efficient Genetic Algorithm (EGA) and Efficient Simulated Annealing (ESA) by sharing process information to obtain as an upper layer optimal operating setpoints for the controller that satisfy the overall process objective. The resulting approach is a unique combination of control and optimization methods that provide optimal solutions for dynamic systems. The applicability of the proposed framework is demonstrated using a nonlinear, multivariable fermentation process. In particular, a multivariable control structure associated with the first-principles-based model derived from mass and energy balances of the fermentation process is addressed. The performance of the proposed approach for each step is compared to Sequential Quadratic Programming (SQP) and a classical Proportional-Integral (PI) controller in terms of optimization and control, respectively. The proposed approach improves the overall performance of the process in terms of cumulative production rate by approximately 10-15%, resulting in economic benefits. The obtained results illustrate the capabilities of this novel integrated framework to achieve desired nonlinear system performance considering scenarios associated with setpoint tracking and plant-model mismatch.
机译：在本文中，提出了一种框架，用于将生物启发的最佳控制策略（BIO-CS）与多代理优化（MAO）算法集成，用于处理系统工程应用。在该框架中，BIO-CS以智能方式采用梯度的最佳控制求解器，以同时控制其所需设定点的过程的多个输出。此外，MAO使用基于非线性启发式的优化技术的能力，例如高效的蚁群优化（EACO），高效的遗传算法（EGA），通过共享处理信息来获得作为上层最佳操作设定点的高效模拟退火（ESA）对于满足整体过程目标的控制器。由此产生的方法是对动态系统提供最佳解决方案的控制和优化方法的独特组合。使用非线性，多变量发酵过程证明了所提出的框架的适用性。特别地，解决了与来自发酵过程的质量和能量余额的基于基于基于原理的模型相关的多变量控制结构。将每个步骤的提出方法的性能分别与优化和控制的顺序二次编程（SQP）和经典比例积分（PI）控制器进行比较。拟议的方法在累积生产率方面提高了该过程的整体性能约10-15％，导致经济效益。所获得的结果说明了考虑与设定点跟踪和植物模型不匹配相关的方案来实现所需的非线性系统性能的本新颖综合框架的能力。

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