Design of Control System for Multistage Distillation Seawater Desalination Device Driven by Photovoltaic-Thermal

Abstract

This research proposes a seawater desalination system driven by photovoltaic and solar thermal energy for remote regions such as islands and seaside villages where fresh water is not accessible. The performance of this system is demonstrated through experiments, and the main concerns are the output of the photovoltaic power generation system, power quantity, water yield, and the loads under different solar irradiance and temperature. In this system, a PLC is used as the controller to adjust the water pump by the collection and processing of sensor data. A load switching time system is designed to select different operating schemes under different environments in order to save energy. The control method of this system is developed to ensure that the photovoltaic power generation system does not undervoltage while maintaining the normal operation of the desalination system. An improved Perturbation and Observation (P&O) algorithm is also proposed as a new Maximum Point Power Tracking (MPPT) method to solve the problem of misjudgment and oscillation after tracking the maximum power point (MPP) in the traditional P&O algorithm. The simulation test in the MATLAB/Simulink environment shows that when external irradiance changes, the improved P&O algorithm can track the MPP faster than the traditional P&O algorithm, and the amplitude of oscillation on the MPP is smaller. The hardware experiments show that this system can operate stably and flexibly, and it is capable of producing 5.18 kWh of electric energy and 335.81 kg of freshwater per day. The maximum yield of the unit can reach 565.75 kg per day and the maximum daily power generation is 8.12 kWh.

Design and Research of a Field Bus Control System Laboratory for Metal Mining, Beneficiation and Metallurgy

“…The current artificial intelligence deep learning algorithms have been able to perform reliable and interpretable optimization control function blocks, especially intelligent con- The current artificial intelligence deep learning algorithms have been able to perform reliable and interpretable optimization control function blocks, especially intelligent control tools such as multivariable model deep learning predictive control [34][35][36][37][38], fuzzy neural network adaptive control, deep learning optimization soft sensor models, etc. Through these algorithm functional models, the production process control effect can be optimized without significantly increasing investment expenses for other software and hardware costs, promoting the goal of reducing the impact on the production process [39,40], especially the current deep learning optimization soft sensor models, based on deep learning and mathematical optimization algorithms, new prediction architectures, data collection methods, fusion model prediction, feedback correction, deep learning, process mathematical modeling, rolling optimization control, and digital twin real-time precise simulation technology. The online real-time collection, monitoring, control, and visualization of various measurement data, even unmeasurable data, that can accurately predict and simulate the production process is of great significance for optimizing the management of production control.…”