A New Hybrid BFOA-PSO Optimization Technique for Decoupling and Robust Control of Two-Coupled Distillation Column Process

Abstract

The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostly, such a process is to be decoupled into several input/output pairings (loops), so that a single controller can be assigned for each loop. In the frame of this research, the Brain Emotional Learning Based Intelligent Controller (BELBIC) forms the control structure for each decoupled loop. The paper's main objective is to develop a parameterization technique for decoupling and control schemes, which ensures robust control behavior. In this regard, the novel optimization technique Bacterial Swarm Optimization (BSO) is utilized for the minimization of summation of the integral time-weighted squared errors (ITSEs) for all control loops. This optimization technique constitutes a hybrid between two techniques, which are the Particle Swarm and Bacterial Foraging algorithms. According to the simulation results, this hybridized technique ensures low mathematical burdens and high decoupling and control accuracy. Moreover, the behavior analysis of the proposed BELBIC shows a remarkable improvement in the time domain behavior and robustness over the conventional PID controller.

Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi-Input Multi-Output Distillation Columns

“…One of these approaches is decreasing gradually (from 0.9 to 0.4) during the iteration. [12,41], the researchers used a set of BELBIC controllers to control every loop/output individually. In other words, they used the single output BELBIC model introduced in [12] rather than MIMO model.…”

“…In other words, they used the single output BELBIC model introduced in [12] rather than MIMO model. In [12,41], they used 2 and 4 BELBIC controllers, respectively. In this research, we introduce MIMO-BELBIC model that can be utilized to control many loops/outputs simultaneously.…”

Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

“…The hybrid BFO-PSO uses the strength of both BFOA and PSO to solve each of the optimization technique's limitations. The problem of BFOA taking a long time to achieve the global optimum is solved by giving the bacteria (E. Coli) the ability to communicate with each other which is obtained from PSO, while on the other hand, PSO's limitation of being stuck in the local optimum is solved by utilizing the chemotaxis steps of BFOA [37]. The inheritance from BFOA and PSO makes the hybrid BFOA-PSO robust and effective in obtaining the optimum solution.…”

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

“…Examples of other hybrid approaches include integration of ANNs with support vector machines (supervised, statistical learning algorithms for data classification) [ 239 ] for model identification, [ 240 ] combination of nature‐inspired algorithm with reinforcement learning for improved controller efficiency, [ 23 ] and simulation of brain emotional learning assisted with PSO to estimate controller parameters. [ 241 ] Some hybrid AI‐based controllers utilize more than one nature‐inspired algorithms to improve efficacy (e.g., with PSO use GAs to mutate poor candidates, [ 26 ] employ differential search to speed‐up local search, [ 223 ] and simulate bacterial foraging to improve global optima [ 225 ] ).…”