For the sake of reducing the total operation cost of grid-connected microgrids, an improved pinning consensus algorithm based on the incremental cost rate (ICR) is proposed, which defines ICR as the state variable. In the algorithm, the power deviation elimination term is introduced to rapidly eliminate the total power deviation, and the pinning term is brought to realize the fast convergence to reference value. By computing the optimal ICR of the system, the optimal active output reference value of each distributed generation (DG) is obtained when the system realizes the economic optimization operation. In addition, an economic optimization control method of grid-connected microgrids, based on improved pinning consensus, is proposed. By utilizing the method, the economic optimization operation of the system is attained by basing on the established distributed hierarchical architecture and by sending the reference value of optimal active output of each DG to the P-f droop control loop.... [more]

The performance of a hybrid solar desalination system is predicted in this work using an enhanced prediction method based on a supervised machine-learning algorithm. A humidification−dehumidification (HDH) unit and a single-stage flashing evaporation (SSF) unit make up the hybrid solar desalination system. The Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) algorithms serve as the foundation for the suggested algorithm. Using experimental data, the BER−PSO algorithm is trained and evaluated. The cold fluid and injected air volume flow rates were the algorithms’ inputs, and their outputs were the hot and cold fluids’ outlet temperatures as well as the pressure drop across the heat exchanger. Both the volume mass flow rate of hot fluid and the input temperatures of hot and cold fluids are regarded as constants. The results obtained show the great ability of the proposed BER−PSO method to identify the nonlinear link between operating circumstances and process responses.... [more]

Wind power ramp events (WPREs) are a common phenomenon in wind power generation. This unavoidable phenomenon poses a great harm to the balance of active power and the stability of frequency in the power supply system, which seriously threatens the safety, stability, and economic operation of the power grid. In order to deal with the impact of ramp events, accurate and rapid detection of ramp events is of great significance for the formulation of response measures. However, some attribute information is ignored in previous studies, and the laws and characteristics of ramp events are difficult to present intuitively. In this paper, we propose a visualization-based ramp event detection model for wind power generation. Firstly, a ramp event detection model is designed considering the multidimensional attributes of ramp events. Then, an uncertainty analysis scheme of ramp events based on the confidence is proposed, enabling users to analyze and judge the detection results of ramp events fro... [more]

The present work proposes an enhanced method of investigation and optimization photovoltaic (PV) modules by approaching and using MPPT (Maximum Power Point Tracking) technique to improve their output power. The performance of the PV panels is strongly influenced by the operating conditions, especially regarding the solar irradiance, temperature, configuration, and the shading (due to a passing cloud or neighboring buildings); all these cause, both on energy conversion loss, and further on non-linearity of the I-V characteristics. From this reason, the present study could have a high relevance based on the improvement of the performances (including the efficiency) of the shaded photovoltaic panels and would quantify the impact of a complex approach represented by numerical modeling and experimental validation. For a better understanding of these issues determined by partial shading, and improvement of MPP tracking, it is required to study the behavior of individual panels. For the best... [more]

Metaheuristic optimization techniques have successfully been used to solve the Optimal Power Flow (OPF) problem, addressing the shortcomings of mathematical optimization techniques. Two of the most popular metaheuristics are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The literature surrounding GA and PSO OPF is vast and not adequately organized. This work filled this gap by reviewing the most prominent works and analyzing the different traits of GA OPF works along seven axes, and of PSO OPF along four axes. Subsequently, cross-comparison between GA and PSO OPF works was undertaken, using the reported results of the reviewed works that use the IEEE 30-bus network to assess the performance and accuracy of each method. Where possible, the practices used in GA and PSO OPF were compared with literature suggestions from other domains. The cross-comparison aimed to act as a first step towards the standardization of GA and PSO OPF, as it can be used to draw preliminary c... [more]

Conventional methods used to determine pyrolysis gas composition are based on chemical kinetics. The mechanism of those reactions is often unknown, which makes the calculations more difficult. Solving complex chemical reactions’ kinetics involving a nonlinear set of equations is CPU time demanding. An alternative approach is based on the Gibbs free energy minimization method. It requires only the initial composition and operation parameters as the input data, for example, temperature and pressure. In this paper, the method for calculating the pyrolytic gas composition from biogenic fuels has been presented, and the thermogravimetric experimental results have been adopted to determine the total gas yield. The studied problem has been reduced to the optimization method with the use of the Lagrange multipliers. This solution procedure is advantageous since it does not require knowledge of the reaction mechanism. The obtained results are in good agreement with experimental data, demonstrat... [more]

This paper proposes an impedance-accelerated inverse-time over-current protection optimization scheme based on the improved quantum genetic algorithm. First, the speed of remote backup protection is improved by increasing the optimization level of backup protection. Second, to ensure the coordination of protection when the distributed generation is connected to the distribution network, a mathematical model for the optimization of inverse time protection parameters is established. The mathematical model takes the minimum total action time of the optimized main and backup protection as the objective function, and the selectivity and sensitivity requirements of the protection as the constraints. In addition, the genetic algorithm is improved from four aspects: coding method, population initialization, quantum revolving gate, and variational evolution. The theoretical analysis and simulation results show that the proposed scheme can effectively improve the selectivity and operation speed... [more]

This paper introduces a brand-new, inspired optimization algorithm (the chaotic billiards optimization (C-BO) approach) to effectively develop the optimal parameters for fuzzy PID techniques to enhance the dynamic response of the solar−hydrogen drive of an induction motor. This study compares fuzzy-PID-based C-BO regulators to fuzzy PID regulators based on particle swarm optimization (PSO) and PI-based PSO regulators to provide speed control in solar−hydrogen, induction-motor drive systems. The model is implemented to simulate the production and storage of hydrogen while powering an induction-motor drive which provides a great solution for the renewable energy storage problem in the case of solar pumping systems. MATLAB/Simulink 2021a is used to simulate and analyze the entire operation. The laboratory prototype is implemented in real time using a DSP-DS1104 board. Based on the simulation and experimental results, the proposed fuzzy-PID-based C-BO has reduced speed peak overshoot by 45... [more]

Daylight penetration significantly affects building thermal-daylighting performance, and serve a dual function of permitting sunlight and creating a pleasant indoor environment. More recent attention has focused on the provision of daylight in the rear part of indoor spaces in designing sustainable buildings. Passive Anidolic Daylighting Systems (ADS) are effective tools for daylight collection and redistribution of sunlight towards the back of the room. As affordable and low-maintenance systems, they can provide indoor daylight and alleviate the problem of daylight over-provision near the window and under-provision in the rear part of the room. Much of the current literature on the ADS pays particular attention to visual comfort and rarely to thermal comfort. Therefore, a reasonable compromise between visual and thermal comfort as well as energy consumption becomes the main issue for energy-optimized aperture design in the tropics and subtropics, in cities such as Sydney, Australia. T... [more]

Fans as industrial devices are among the most significant single recipients of driving energy. Therefore, they represent an important area of energy savings to reduce CO2 emissions. The ubiquity of fans and their operation under conditions different from the optimum provides an opportunity for more significant global reductions in the energy used to drive them. The second important aspect, besides energy efficiency, related to the operation of fans is the noise they generate. This article studies fans in various operating configurations, emphasizing improving operating efficiency and reducing noise emissions. The subject matter undertaken is based on the current trends and needs of the industry, notably the ventilation and power industry. The article attempts a detailed literature review taking into account just these aspects. The paper is divided into three main sections, with an analysis of the literature in the areas of energy efficiency, considering the operation of fans with the n... [more]

This research proposed a novel global maximum power point tracking (global-MPPT) algorithm. The proposed algorithm eliminates the perturbation and observation (P&O) technique disturbance problem that the power point will be stuck at the local peak power point under a partial shading condition (PSC). The proposed global-MPPT algorithm detects the photovoltaic module (PV-M) environment irradiance level by the relationship between the output power and voltage of the PV-M. In the proposed algorithm, the important parameter w is determined by the PV-M output power and irradiance level, which is also the compensation parameter that corresponds to the relationship of temperature. The proposed global-MPPT algorithm is aimed to predict the best duty cycle of the global-MPPT based on the irradiance level, parameter w, PV-M output voltage, and load, and then achieve the maximum power point (MPP) quickly and accurately. The measurement results under UIC and PSC verify that the proposed global-MPPT... [more]

Dynamic optimization is an important research topic in chemical process control. A dynamic optimization method with good performance can reduce energy consumption and prompt production efficiency. However, the method of solving the problem is complicated in the establishment of the model, and the process of solving the optimal value has a certain degree of difficulty. Based on this, we proposed a non-fixed points discrete method of an enhanced beetle antennae optimization algorithm (EBSO) to solve this kind of problem. Firstly, we converted individual beetles into groups of beetles to search for the best and increase the diversity of the population. Secondly, we introduced a balanced direction strategy, which explored extreme values in new directions before the beetles updated their positions. Finally, a spiral flight mechanism was introduced to change the situation of the beetles flying straight toward the tentacles to prevent the traditional algorithm from easily falling into a certa... [more]

Industrial processes provide several of the products and services required for society. However, each industry faces different challenges from different perspectives, all of which must be reconciled to obtain profitable, productive, controllable, safe and sustainable processes. In this context, multi-objective optimization has become a powerful tool to aid the decision-making mechanism in the synthesis, design, operation and control of such processes. The solution to the mathematical models provides the necessary tools to asses the system performance in terms of different metrics and evaluate the trade-offs between the objectives in conflict. The number of applications of multi- objective optimization in industrial processes is ample and each application has its own challenges. In the present literature review, a broad panorama of the applications in multi-objective optimization is presented, including future perspectives and open questions that still need to be addressed.

The main objective of the present work was to study the drying kinetics and obtain the optimum process parameters of cantaloupe slices using short-and medium-wave infrared radiation (SMIR) drying technology. The effect of three independent variables of infrared radiation temperature (55−65 °C), slice thickness (5−9 mm) and radiation distance (80−160 mm) on the L value, color difference (∆E), hardness and vitamin C content were investigated by using the Response Surface Methodology (RSM). The results showed that the Page model can adequately predict the moisture content between 55 and 65 °C (R2 > 0.99). The effective moisture diffusivity (Deff) varied from 5.26 × 10−10 to 2.09 × 10−9 m2/s and the activation energy (Ea) of the SMIR drying was 31.84 kJ/mol. Infrared radiation temperature and slice thickness exerted extremely significant effects on L value and color difference (ΔE) (p < 0.01), with higher infrared radiation temperature and thin slice thickness leading to a decrease in t... [more]

Due to the uncertainty of wind power output, the congestion of wind power has become prominent. Exactly how to improve the capacity of wind power consumption has become a problem that needs to be studied urgently. In this paper, an energy storage system and energy-extensive load with adjustable characteristics are used as an important means of consuming wind power. Firstly, we analyze the reasons for the congestion according to the characteristics of wind power output, and establish a model of the grid’s ability to integrate wind power based on the concept of a wind power admissible interval. Secondly, we analyze the energy-extensive load regulation characteristics and establish an energy-extensive load dispatch model. Thirdly, on the basis of considering the energy-extensive load and energy storage system adjustment constraints, a bi-level optimization model is established. The upper level determines the configured capacity of the energy storage system with the goal of minimizing the... [more]

In Industry 4.0, data are sensed and merged to drive intelligent systems. This research focuses on the optimization of selective assembly of complex mechanical products (CMPs) under intelligent system environment conditions. For the batch assembly of CMPs, it is difficult to obtain the best combinations of components from combinations for simultaneous optimization of success rate and multiple assembly quality. Hence, the Taguchi quality loss function was used to quantitatively evaluate each assembly quality and the assembly success rate is combined to establish a many-objective optimization model. The crossover and mutation operators were improved to enhance the ability of NSGA-III to obtain high-quality solution set and jump out of a local optimal solution, and the Pareto optimal solution set was obtained accordingly. Finally, considering the production mode of Human−Machine Intelligent System interaction, the optimal compromise solution is obtained by using fuzzy theory, entropy theo... [more]

Throughout the past several years, the renewable energy contribution and particularly the contribution of wind energy to electrical grid systems increased significantly, along with the problem of keeping the systems stable. This article presents a new optimization technique entitled the Archimedes optimization algorithm (AOA) that enhances the wind energy conversion system’s stability, integrated with a superconducting magnetic energy storage (SMES) system that uses a proportional integral (PI) controller. The AOA is a modern population technique based on Archimedes’ law of physics. The SMES system has a big impact in integrating wind generators with the electrical grid by regulating the output of wind generators and strengthening the power system’s performance. In this study, the AOA was employed to determine the optimum conditions of the PI controller that regulates the charging and discharging of the SMES system. The simulation outcomes of the AOA, the genetic algorithm (GA), and pa... [more]

Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector Machine (SVM) is adopted as the classifier for classification purposes in this paper. We proposed an enhanced Teaching-Learning-Based Optimization (ETLBO) to optimize the SVM and Tsallis entropy parameters to improve classification accuracy. The adaptive weight strategy and Kent chaotic map are used to enhance the optimal ability of the traditional TLBO. The proposed method aims to avoid the main weaknesses of the original TLBO, which is trapped in local optimal and unbalance between the search mechanisms. Experiments based on 16 classical datasets are selected to test the performance of the ETLBO, and the results are compared with other well-established optimization algorithms. The obtained results illustrate that the proposed method has... [more]

The vacuum distillation unit (VDU) is the key unit to produce vacuum gas oil and vacuum residue, which has a very important impact on the downstream secondary processing units. The optimization of deep-cut vacuum distillation seeks to improve the yield of heavy vacuum gas oil (HVGO) and its dry point temperature, which is related to the economic benefits of the refinery. In this study, we first established a simple model of a VDU by using the Aspen HYSYS Process simulation software. Then, we built a rigorous model with fast convergence by using the initial values obtained by the simple model. The rigorous model can accurately reflect the refinery’s operation and can make predictions. Then, based on the rigorous model, we increased the flash section temperature (FST) to 420 °C and the steam flow rate (SFR) of the stripping to 26 t/h. We eventually increased the yield of HVGO by 6.3 percentage points to 43.4%, while increasing its D86 95%-point temperature by 31.9 °C to 570.9 °C. In this... [more]

The application of integrated energy systems is significant for realizing the comprehensive utilization of various energy sources and improving the utilization rate of renewable energy. At present, the optimal operation of integrated energy systems is a research hotspot. However, shortcomings remain in the stochastic optimization operation and the scenario generation method. This paper proposes a stochastic optimization operation model of an integrated energy microgrid based on an advanced multi-scenario generation method. First, this paper establishes the time-divided probability distribution model of the forecasting error of the uncertain factors, such as photovoltaic (PV) power and load, which provide the basis for generating scenarios. Moreover, the covariance matrix is used to calculate the time correlation of the time-divided probabilistic distributed models, and the parameters of the covariance matrix are optimized. Second, based on multiple typical scenarios, the stochastic opt... [more]

The dilution rate of surfacing layers and the quality of weld forming are important factors affecting the quality of surfacing layers in open-arc surfacing. They are determined by the interaction of various surfacing parameters. In this paper, the response surface method is used to optimize the process parameters of open-arc surfacing welding. Mathematical models of the surfacing current, surfacing voltage, surfacing speed, dilution rate and weld residual height were established, and the reliability of the models was verified by variance analysis. By performing an analysis of the perturbation diagram and response surface diagram, the influence law of each influencing factor on the response value was obtained. The parameters of surfacing welding were optimized by setting optimization targets, and the experimental results of optimized parameters were compared with the predicted results. The optimized surfacing parameters were tested by grinding roller surfacing repair. The experimental r... [more]

An extended membrane system using a tissue-like P system with evolutional symport/antiport rules and a promoter/inhibitor, which is based on the evolutionary mechanism of quantum-behaved particle swarm optimization (QPSO) and improved QPSO, named CQPSO-ETP, is designed and developed in this paper. The purpose of CQPSO-ETP is to enhance the optimization performance of statistical network structure-based membrane-inspired evolutionary algorithms (SNS-based MIEAs) and the QPSO technique. In CQPSO-ETP, evolution rules with a promoter based on a standard QPSO mechanism are introduced to evolve objects, and evolution rules with an inhibitor based on an improved QPSO mechanism using self-adaptive selection, and cooperative evolutionary and logistic chaotic mapping methods, are adopted to avoid prematurity. The communication rules with a promoter/inhibitor for objects are introduced to achieve the exchange and sharing of information between different membranes. Under the control of the evoluti... [more]

A Steam generator is a crucial device of a nuclear power plant. Control performance of the steam generator level control system is key to its normal operation. To improve its performance, the control system parameters should be optimized by utilizing a proper optimization method. Furthermore, the method’s efficiency is critical for its operability in the actual plant. However, the steam generator level process is a complex process, with high nonlinearity and time-varying properties. Traditional parameters tuning methods are experience-based, cumbersome, and time-consuming. To address the challenge, a systemic data-driven optimization methodology based on the model-free optimization with a revised simplex search method was proposed. Rather than the traditional controller parameter tuning method, this method optimizes the control system directly by using control performance measurements. To strengthen its efficiency, two critical modifications were incorporated into the traditional simpl... [more]

We consider one-step and two-step simple models of anaerobic digestion that are able to adequately capture the main dynamical behaviour of the full anaerobic digestion model ADM1. We do not consider specific growth functions. We only require them to satisfy certain qualitative assumptions. These assumptions are satisfied for concave growth functions, but they are also satisfied for a large class of growth functions found in many applications. We consider the maximisation of the biogas production with respect to the operating parameters of the model, which are the dilution rate and the substrate input concentration. We give the best operating conditions and we describe them as a subset of the set of operating parameters. Our models incorporate biomass decay terms, corresponding to maintenance. Numerical plots with specified growth functions and biological parameters illustrate the obtained results.

In industrial practice, excessive alarms and high alarm rates are mostly generated from unreasonable settings to variable alarm thresholds, which have become the significant causes of impact on operation stability and plant safety. A correlation degree and clustering analysis-based approach was presented to optimize the variable alarm thresholds in this paper. The correlation degrees of variables are first obtained by analyzing correlation relationships among them. Second, the variables are grouped according to the gray correlation coefficients and clustering analysis, given the weight for fault alarm rate (FAR) in each group. An objective function about the FAR, missed alarm rate (MAR), and the maximum acceptable FAR and MAR is then established with variable weight. Eventually, based on an optimization algorithm, the objective function can be optimized for obtaining the optimal alarm threshold. Cases study of the Tennessee Eastman (TE) industrial simulation process and an actual indus... [more]