(569f) Design and Implementation of a Biomimetic Control Strategy for Chemical Processes Based on Efficient Ant Colony Optimization

In the last decades, many biological systems have been a source of inspiration for advanced control methods because of the successes of these systems in solving difficult problems encountered in nature. For example, the behavior of natural groups such as ants, bees and swarms demonstrates that self-organization and cooperation by following simple rules of interaction can result in a wide range of optimal patterns [1]. Inspired by the ant’s rule of pursuit idea, the present work introduces a novel biomimetic optimal control strategy for nonlinear chemical process control. In this approach, starting from an initially feasible trajectory for the leader agent (or ant), each follower agent improves its path towards the setpoint by employing optimal control laws. As the number of agent progresses, the trajectories converge to an optimal solution. The proposed approach overcomes the computational time limitations of the previously introduced algorithm based on the modified version of the Generalized Sampled Local Pursuit (GSLP) strategy [2]. The previous strategy used the gradient-based solver dynopt [3] to tackle the intermediate optimal control problems for the leader-follower local interactions. To address the computational challenges of the gradient-based approach, the present biomimetic controller framework employs the computationally efficient optimal control solver entitled Efficient Ant Colony Optimization (EACO).

The EACO algorithm is a metaheuristic optimization technique inspired by the ants’ foraging behavior which utilizes probabilistic and stochastic concepts for solving large-scale optimization problems. In basic Ant Colony Optimization (ACO) algorithms, artificial ants are stochastic candidate solution construction procedures that exploit a pheromone model and possibly available heuristic information of the mathematical model. The artificial pheromone trails are the sole means of communication among the artificial ants. Pheromone decay allows the artificial ants to forget the past history and focus on new promising search directions. The pheromone values are updated according to the information learned and the algorithmic procedure leads to accurate and potentially, a global optimal solution [4]. The EACO algorithm improves the performance of the conventional ACO algorithm for combinatorial, continuous and mixed-variable optimization problems by using the Hammersley sequence sampling (HSS) for probabilistic elements of ACO. The initial solution archive diversity for continuous and mixed-variable optimization problems plays an important role in the performance of the ACO algorithm. The uniformity property of the HSS technique is exploited to avoid clustering of the initial solution archive in a small region of the potential solution space. Moreover, the ACO algorithm is a probabilistic method, in which several random probability functions are involved in the algorithm procedure. The distribution of these random numbers affects the performance of the ACO algorithm. At this step, the multidimensional uniformity property of HSS is also introduced to improve the computational efficiency of the ACO algorithm. The capabilities of the proposed methodology were illustrated using benchmark problems and real world case studies [5].

The developed biomimetic controller approach consists of the novel combination of the EACO technique with the modified GSLP strategy for chemical process control. The applicability of the proposed strategy is demonstrated by its implementation for the optimal control of a fermentation process in which the objective is to track the product concentration setpoint. The challenges in this fermentation process are the steady-state multiplicity and the oscillations in the concentration profiles that affect process efficiency [6]. Closed-loop results associated with this implementation show that the biomimetic controller successfully overcomes these process challenges. In addition, the performance of the proposed approach is compared to the classical PI controller as well as to the modified GSLP-dynopt strategy. The results of such comparisons indicate potential improvement in the solution quality and reduction in computational time for the biomimetic controller when compared to its counterparts. Therefore, the proposed GSLP-EACO control approach provides a computationally efficient alternative to the gradient-based optimal control algorithms for optimization of large-scale dynamic systems.

References:

[1] Hristu-Varsakelis D. and Shao C. “A bio-inspired pursuit strategy for optimal control with partially constrained final state”. Automatica 2007;43:1265-1273.

[2] Lima F. V., Li S., Mirlekar G. V., Sridhar L. N. and Ruiz-Mercado G. J., “Modeling and advanced control for sustainable process systems”. Submitted to Sustainability in the Analysis, Synthesis and Design of Chemical Engineering Processes, G. Ruiz-Mercado and H. Cabezas (eds.), Elsevier, 2015.

[3] Cizniar M., Fikar M., Latifi M. A., “MATLAB DYNamic OPTimisation code”, User’s Guide, Version 4.1, 2010.

[4] Dorigo, M., Stutzle, T., “Ant colony optimization theory”. A Brandford Book, the MIT Press, Cambridge, Massachusetts, 2004.

[5] Gebreslassie B. H. and Diwekar U. M., “Efficient ant colony optimization (EACO) for computer aided molecular design: Case study solvent selection problem”. Computers and Chemical Engineering 2015:78:1-9.

[6] Sridhar L. N. “Elimination of oscillations in fermentation processes”. AIChE Journal 2011;57(9):2397-2405.