Neuro-estimator based Generic Model Control of a Non-linear CSTR having Multiplicity

School of Mechanical Engineering, VIT Vellore, Vellore, 632 014, Tamil Nadu, India

School of Electrical Engineering, VIT Vellore, Vellore, 632 014, Tamil Nadu, India

Chemical Engineering Department, Motilal Nehru National Institute of Technology, Allahabad, 211 004, Uttar Pradesh, India.

E-mail: dipesh-patle@mnnit.ac.in

Manuscript Received online 7/24/2020, Accepted 8/23/2020

The control of a non-linear jacketed Continuous Stirred Tank Reactor (CSTR) with steady-state multiplicity is challenging due to its unstable nature. Generally, CSTR is operated near/at unstable equilibrium nodes, which decides the optimal productivity of the process. In this paper, a neural-estimator based non-linear control structure is developed for a CSTR possessing multiplicity. A Neuro-estimator based on feed-forward neural network has been designed to estimate the reactor concentration, which is often an imprecisely known parameter of the CSTR. We integrate the Neuro-estimator with a generic model controller (GMC) to develop a Neuro-GMC structure which utilizes the concentration estimated by the Neuro-estimator. Both servo and regulatory studiesare performed to assess the effectiveness of the Neuro-GMC in controlling the reactor. Two additional control schemes, namely an extended Internal Model Control (IMC) and a standard PI controller, are designedto compare performance of the designed Neuro-GMC. Simulation results highlight that even in the presence of process-model mismatch,the Neuro-GMC yields better tracking and disturbance rejection characteristics.