Abstract In this work, we develop model predictive control (MPC) designs, which are capable of optimizing closed‐loop performance with respect to general economic considerations for a broad class nonlinear process systems. Specifically, in the proposed MPC optimizes cost function, is related directly desired and not necessarily dependent on steady‐state—unlike conventional designs. First, consider systems synchronous measurement sampling uncertain variables. The designed via Lyapunov‐based...

Abstract This work focuses on a class of nonlinear control problems that arise when new systems which may use networked sensors and/or actuators are added to already operating loops improve closed‐loop performance. In this case, it is desirable design the pre‐existing system and in way such they coordinate their actions. To address problem, distributed model predictive method introduced where both designed via Lyapunov‐based control. Working with general models chemical processes assuming...

Abstract In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets manipulated inputs are used to regulate the process. For each set inputs, a different controller is compute actions, able communicate with rest controllers making its decisions. Under assumption that feedback state available all at sampling time and plant available, propose two architectures. first architecture, use one‐directional communication...

This work focuses on the development of a supervisory model predictive control method for optimal management and operation hybrid standalone wind-solar energy generation systems. We design system via which computes power references wind solar subsystems at each sampling time while minimizing suitable cost function. The are sent to two local controllers drive requested references. discuss how incorporate practical considerations, example, extend life equipment by reducing peak values inrush...

Wastewater treatment is an integral component in the sustainable development of our society. Optimal control and operation critical to efficiency economics a wastewater plant. In this work, we apply economic model predictive (EMPC) plant compare its performance with two commonly used methods. Specifically, take advantage benchmark simulation no. 1 provided by International Water Association simulate biological A computationally efficient EMPC developed recently adopted work optimize effluent...

In this work, we design a distributed supervisory model predictive control (MPC) system for optimal management and operation of wind solar energy generation systems integrated into the electrical grid to facilitate development so-called "smart grid". We consider topology in which two spatially systems, subsystem subsystem, are DC power grid, providing local area, each is coupled with an storage device. A MPC optimization problem first formulated take account optimality considerations on...

In this work, we focus on iterative distributed model predictive control (DMPC) of large-scale nonlinear systems subject to asynchronous, delayed state feedback. The motivation for studying problem is the presence measurement samplings in chemical processes and potential use networked sensors actuators industrial process applications improve closed-loop performance. Under assumption that there exist upper bounds time interval between two successive measurements maximum delay, design an DMPC...

In this work, we design a supervisory control system via model predictive (MPC) for the optimal management and operation of an integrated wind-solar energy generation reverse-osmosis (RO) water desalination system. The MPC is able to coordinate wind solar subsystems as well battery bank provide enough RO subsystem so that desalinated can be produced satisfy consumption storage demands. Optimality considerations on savings are also taken into account appropriate constraints in controller...

Accurate detection and analysis of traces persistent organic pollutants in water is important many areas, including environmental monitoring food quality control, due to their long stability potential bioaccumulation. While conventional requires expensive equipment, surface enhanced Raman spectroscopy (SERS) has demonstrated great for accurate these contaminants. However, SERS analytical difficulties, such as spectral preprocessing, denoising, substrate-based variation, have hindered...

Amid concerns about freshwater scarcity, the agricultural sector faces challenges in water conservation and optimizing crop yields, highlighting limitations of traditional irrigation scheduling methods. To overcome these challenges, this paper introduces a unified, learning-based predictive scheduler that integrates machine learning Model Predictive Control (MPC), while also incorporating multi-agent principles. The proposed framework incorporates three-stage management zone delineation...