Multi-Model MPC for a Complex pH Neutralization Process
- Type: Conference Presentation
- Skill Level:
You will be able to download and print a certificate for PDH credits once the content has been viewed. If you have already viewed this content, please click here to login.
Control of feed pH control is an important task in chemical industries, wastewater treatment, polymerization reactions, fatty acid production and biochemical processes. However, this is a challenging problem due to (i) presence of large and varying time delays and (ii) complex time varying nonlinear relation exhibited by the titration curves. Several advanced control algorithms like multi-model predictive control and neuro-controllers have been used to address the pH control problem. However, the corresponding drawbacks in these approaches are: (i) model-plant mismatch (plant dynamics constantly varies) and (ii) computational complexity involved in designing of a single neuro-controller. In this work, a novel genetic algorithm based neural multi-model predictive controller is developed to control the highly nonlinear and challenging pH control problem. The advantages of the proposed approach are (i) multi-model predictive control approach reduces the computational burden involved in designing a single nonlinear controller and (ii) neural network model can provide better approximation of the process over various regions compared to the linear models and (iii) genetic algorithm, a global optimization approach used to obtain the number of hiden nodes and connecting weights of the multiple neural models helps in reducing the model-plant mismatch that could occur in various regimes. The proposed approach is demonstrated on a wastewater neutralization process and a comparison of results with other commonly used approaches is provided. Simulations studies illustrate the practicality and utility of the proposed scheme to control nonlinear processes.