Environmental Health Oriented Optimal Temperature Control for Refrigeration Systems Based on a Fruit Fly Intelligent Algorithm

Abstract

The recent decades have witnessed refrigeration systems playing an important role in the life of human beings, with wide applications in various fields, including building comfort, food storage, food transportation and the medical special care units. However, if the temperature is not controlled well, it will lead to many harmful public health effects, such as the human being catching colds, food spoilage and harm to the recovering patients. Besides, refrigeration systems consume a significant portion of the whole society’s electricity usage, which consequently contributes a considerable amount of carbon emissions into the public environment. In order to protect human health and improve the energy efficiency, an optimal control strategy is designed in this paper with the following steps: (1) identifying the refrigeration system model based on a least squares method; (2) tuning an initial group of parameters of the proportional-integral-derivative (PID) controller via the pidTuner Toolbox of Matlab; (3) using an intelligent algorithm, namely fruit fly optimization (FOA), to further optimize the parameters of the PID controller. By comparing the optimal PID controller and the controller provided in the reference, the simulation results demonstrate that the proposed optimal PID controller can produce a more controllable temperature, with less tacking overshoot, less settling time, and more stable performance under a constant set-point.

Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization

“…where kp, ki, and kd are the proportional gain, integral gain, and derivative gain, respectively. In previous research, the main work has been to search for the optimal values of kp, ki, and kd [17,19,28].…”

“…where k p , k i , and k d are the proportional gain, integral gain, and derivative gain, respectively. In previous research, the main work has been to search for the optimal values of k p , k i , and k d [17,19,28]. However, the filter coefficient N is also of great importance, and an appropriate filter coefficient can reduce the output oscillation and improve the control performance.…”

“…To this end, numerous artificial intelligence (AI)-based optimization methods have been applied to tune PID controllers. For example, Qin, Sun, and Hua [17] used a fruit fly optimization algorithm (FOA) to optimize the PID controllers of a refrigeration system, Zamani et al [18] applied a particle swarm optimization (PSO) algorithm to optimize the PID controller of an automatic voltage regulator (AVR), and Elbayomy et al [19] optimized the PID controller of an electrohydraulic servo actuator system (EHSAS) to control the movable surface of a space vehicle. However, these references all used a single-objective optimization method, where the authors judged a controller's performance through one objective function that weighted the sum of different performance indices, such as the integral absolute error (IAE), overshoot, and settling time.…”

Intelligent Modeling and Design of a Novel Temperature Control System for a Cantilever-Based Gas-Sensitive Material Analyzer

“…After establishing the ''black box'' model of the system through an LSTM network, evolutionary algorithms are used to search for the optimal PID control parameters of the system. Here, we consider the artificial sheep algorithm (ASA) [29], particle swarm optimization (PSO) [30], [31], genetic algorithm (GA) [32], and fruit-fly optimization algorithm (FOA) [33]. Compared with other evolutionary methods, PSO can generate high-quality solutions quickly and has more stable convergence characteristics [34].…”

A Data-Driven-Based Industrial Refrigeration Optimization Method Considering Demand Forecasting

“…Nevertheless, the time needed to calculate the optimal PLR remains an issue in order to maintain the stability of the controlled asset [13]. In refrigeration systems, it is very important to maintain the product in a specific temperature range to preserve its quality and, hence, fast changes in setpoints are necessary in order to preserve the desired temperature [14]. In this regard, many approaches are searching for the optimal PLR by considering steady operational conditions within a specific time interval, assuming that no further changes or minimal changes appear during the time lapse.…”