Robust decentralized proportional–integral controller design for an activated sludge process

Neural Network-Based Adaptive Tracking Control for Denitrification and Aeration Processes With Time Delays

Wastewater treatment process (WWTP), consisting of a class of physical, chemical, and biological phenomena, is an important means to reduce environmental pollution and improve recycling efficiency of water resources. Considering characteristics of the complexities, uncertainties, nonlinearities, and multitime delays in WWTPs, an adaptive neural controller is presented to achieve the satisfying control performance for WWTPs. With the advantages of radial basis function neural networks (RBF NNs), the unknown dynamics in WWTPs are identified. Based on the mechanistic analysis, the time-varying delayed models of the denitrification and aeration processes are established. Based on the established delayed models, the Lyapunov-Krasovskii functional (LKF) is used to compensate for the time-varying delays caused by the push-flow and recycle flow phenomenon. The barrier Lyapunov function (BLF) is used to ensure that the dissolved oxygen (DO) and nitrate concentrations are always kept within the specified ranges though the time-varying delays and disturbances exist. Using Lyapunov theorem, the stability of the closed-loop system is proven. Finally, the proposed control method is carried out on the benchmark simulation model 1 (BSM1) to verify the effectiveness and practicability.

Robust optimal centralized PI controller for a fluid catalytic cracking unit

Fluidized Catalytic Cracking (FCC) is a complex process that arises due to feed composition, non-linearities, and dynamic mass and heat interactions in its components. FCC is difficult to model and monitor in industries, and one of the key reasons is that they are multivariable processes. Such processes are highly interacting and that makes the process of controlling even more difficult. The interaction between loops can be quantified easily by dRGA. An easy and effective way of controlling multivariable processes is to implement a centralized control system, considering the interactions between measured and manipulated variables. In this study, a centralized control system is designed for the riser section of the FCC unit. The dRGA method is modified to enhance the closed-loop response by formulating an optimization problem and obtaining an optimal controller settings. A rigorous simulation studies show an 826% reduction in ISE values, a 309% reduction in IAE values, and a 262% reduction in ITAE value of T r i s ${T}_{ris}$ from the dRGA method to the modified dRGA method. Further, IAE values for Y l p g ${Y}_{lpg}$ are reduced by 29% from dRGA to modified dRGA method and 34% from synthesis to modified dRGA method.