Evaluating the efficiency of CCHP systems in Xinjiang Uygur Autonomous Region: An optimal strategy based on improved mother optimization algorithm

A novel approach is presented in this research to improve the design of a combined cooling, heating, and power (CCHP) system. The focus of the study is on a gas turbine system that can provide efficient power, cooling, and heating. The efficacy of the proposed technique is evaluated based on four parameters, namely energetic, exergetic, economic, and environmental features. To achieve superior and optimal results, an Improved Mother Optimization Algorithm (IMOA), a recently developed metaheuristic algorithm, is employed. This study provides a comprehensive guide for enhancing the performance, efficiency, and sustainability of CCHP systems. A practical case study was conducted to evaluate the proposed methodology in rural areas of Xinjiang Uygur Autonomous Region, China, over a period of one year. The simulation results reveal that the Gas Engine (GE) and the boiler components play a significant role in utilizing fuel energy, contributing 65 % and 35 %, respectively to the overall utilization. The analysis of the destruction rate of profile highlights that heat dissipation, mechanical losses, and electrical losses are the primary sources of energy losses within the system. When comparing the proposed IMOA with existing techniques, it is evident that the IMOA achieves a noteworthy 10 % reduction in energy consumption and a remarkable 15 % increase in overall efficiency of system. Furthermore, in terms of environmental impact, the IMOA leads to a substantial 20 % reduction in carbon dioxide (CO2) emissions compared to traditional optimization methods. The economic analysis results demonstrate that the IMOA-based approach not only improves cost-effectiveness by 25 %, but also yields an estimated return on investment (ROI) that is 18 % higher than alternative optimization techniques. Moreover, the proposed method with minimum fuel usage of 0.200 L/h, compared with Improved Butterfly Optimizer (IBO) with 0.201 L/h, Modified Mayfly (MM) algorithm with 0.205 L/h, Developed Owl Search (DOS) algorithm with 0.211 L/h, and Improved Owl Search (IOS) algorithm methods with 0.207 L/h, provides better results

Developed Design of Battle Royale Optimizer for the Optimum Identification of Solid Oxide Fuel Cell

One of the most appropriate electricity production systems is solid oxide fuel cells (SOFCs), which are important because they are highly efficient, flexible to fuel, and have fewer environmental degradation effects. A new optimum technique has been provided for providing well-organized unknown parameters identification of the solid oxide fuel cell system. The main idea is to achieve the lowest amount of the sum of square error between the model’s output voltage and the empirical voltage datapoints. To get efficient results, the minimum error value has been achieved by designing a new metaheuristic algorithm, called the Developed version of Battle Royale algorithm. The reason for using this version of Battle Royale algorithm is to achieve results with higher accuracy and better convergence. The proposed technique was then applied to a 96-cell SOFC stack under different temperature and pressure values and its achievements were compared with several different latest methods to show the proposed method’s efficiency

An innovative technique for optimization and sensitivity analysis of a PV/DG/BESS based on converged Henry gas solubility optimizer: A case study

Abstract The construction of hybrid power plants with renewable resources can bring significant economic benefits if it is evaluated economically and technically. The present study uses a novel optimum methodology for designing a combined solar/battery/diesel system in Yarkant, Xinjiang Uyghur Autonomous Region of China. In the desired system, the green energy combined system is designed to reduce the use of diesel generators. The diesel generator has been used in the photovoltaic, diesel, and battery to support green energy resources and batteries, as well as function as a backup generator for critical times whenever the production of green energy resources is low or the load demand is high. The amount of CO2 emitted, the probability of load shortage and the system cost on yearly basis are the major goals in the process of optimization. Here, the single‐objective problem is created by using the ε‐constraint technique to combine the many objectives. An improved Henry gas solubility optimizer handles the problem of optimization. To demonstrate the superiority of the strategy, a comparison is conducted between the simulation outcomes of the offered system, HOMER, and particle swarm optimizer ‐based optimum systems from the literature. The sensitivity of each parameter is also examined using sensitivity analysis

SqueezeNet for the forecasting of the energy demand using a combined version of the sewing training-based optimization algorithm

With the introduction of various loads and dispersed production units to the system in recent years, the significance of precise forecasting for short, long, and medium loads have already been recognized. It is important to analyze the power system’s performance in real-time and the appropriate response to changes in the electric load to make the best use of energy systems. Electric load forecasting for a long period in the time domain enables energy producers to increase grid stability, reduce equipment failures and production unit outages, and guarantee the dependability of electricity output. In this study, SqueezeNet is first used to obtain the required power demand forecast at the user end. The structure of the SqueezeNet is then enhanced using a customized version of the Sewing Training-Based Optimizer. A comparison between the results of the suggested method and those of some other published techniques is then implemented after the method has been applied to a typical case study with three different types of demands-short, long, and medium-term. A time window has been set up to collect the objective and input data from the customer at intervals of 20 min, allowing for highly effective neural network training. The results showed that the proposed method with 0.48, 0.49, and 0.53 MSE for Forecasting the short-term, medium-term, and long-term electricity provided the best results with the highest accuracy. The outcomes show that employing the suggested technique is a viable option for energy consumption forecasting

The incidence of complications of vaccination in children and infants of Semnan, Iran

Introduction: Vaccination has achieved significant and cost effective success in prevention ofinfectious disease. Although the current vaccines used are very effective and their side effects areminimal, generally no vaccine is not free from side effects. Incidence of adverse reactions afterimmunization may discourage people for further immunization of their children. The aim of this workwas to determine the incidence of complications of vaccination in children and infants of Semnan, Iran.Materials and methods: In a longitudinal study (prospective), all vaccinated children betweenSeptember 2006 and March 2007 in 11 vaccination centers were studied. A specific questionnaire,including vaccine recipient profile, type of vaccine, birth weight, feeding and 46 adverse reactions werecompleted immediately after and 2,4,6,12,18 months later.Results: 5776 children were studied. 29% of the children showed at least one adverse reaction ofvaccination. The most common adverse reactions were: fever (24%), pain at injection site (3.8%),swelling (2.5%), erythema (2.5%), induration (2.1%), and ulceration at injection site (2.1%). Incidence ofother complications was below 1%. The most dangerous complication of the vaccine was encephalitis(one case) and two cases had febrile seizures. The most cause of hospitalization was adverse reaction ofMMR vaccine. Finally, of every four children, one child showed at least one complication that fever wasthe most common.Conclusion: In general, routine immunization program of Iran country against nine common infectiousdiseases has lower complications. This complication is mainly mild and transient and do not need anyintervention by drugs. However, among these may be rare and dangerous complications such as seizuresand encephalitis occur. Hence, a careful follow-up program is required to report complications ofimmunizatio