An Integrated Biomimetic Control Strategy with Multi-agent Optimization for Nonlinear Chemical Processes⁎. Issue 18 (2018)

• Record Type: Journal Article
• Authors: Mirlekar, Gaurav; Gebreslassie, Berhane H.; Li, Shuyun; Diwekar, Urmila M.; Lima, Fernando V.
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 18(2018)
• Page Start: 55
• Page End: 60
• Publication Date: 2018
• Subjects: Nonlinear Control -- Optimal Control -- Agents -- Optimization -- Intelligent Control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.248
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.