One way to increase overall natural gas engine efficiency is to transform exhaust waste heat into useful energy by means of a bottoming cycle. Organic Rankine cycle (ORC) is a promising technology to convert medium and low grade waste heat into mechanical power and electricity. This paper presents an energy and exergy analysis of three ORC-Waste heat recovery configurations by using an intermediate thermal oil circuit: Simple ORC (SORC), ORC with Recuperator (RORC) and ORC with Double Pressure (DORC), and Cyclohexane, Toluene and Acetone have been proposed as working fluids. An energy and exergy thermodynamic model is proposed to evaluate each configuration performance, while available exhaust thermal energy variation under different engine loads was determined through an experimentally validated mathematical model. Additionally, the effect of evaportating pressure on net power output , absolute thermal efficiency increase, absolute specific fuel consumption decrease, overall energy conversion efficiency, and component exergy destruction is also investigated. Results evidence an improvement in operational performance for heat recovery through RORC with Toluene at an evaporation pressure of 3.4 MPa, achieving 146.25 kW of net power output, 11.58% of overall conversion efficiency, 28.4% of ORC thermal efficiency, and an specific fuel consumption reduction of 7.67% at a 1482 rpm engine speed, a 120.2 L/min natural gas Flow, 1.784 lambda, and 1758.77 kW mechanical engine power.

Given the increase in population and energy demand worldwide, alternative methods have been adopted for the production of hydrogen as a clean energy source. This energy offers an alternative energy source due to its high energy content, and without emissions to the environment. In this bibliometric analysis of energy production using electrolysis and taking into account the different forms of energy production. In this analysis, it was possible to evaluate the research trends based on the literature in the Scopus database during the years 2011–2021. The results showed a growing interest in hydrogen production from electrolysis and other mechanisms, with China being the country with the highest number of publications and the United States TOP in citations. The trend shows that during the first four years of this study (2011–2014), the average number of publications was 74 articles per year, from 2015 to 2021 where the growth is an average of 209 articles, the journal that published the most on this topic is Applied Energy, followed by Energy, contributing with almost 33% in the research area. Lastly, the keyword analysis identified six important research points for future discussions, which we have termed clusters. The study concludes that new perspectives on clean hydrogen energy generation, environmental impacts, and social acceptance could contribute to the positive evolution of the hydrogen energy industry.

A hybrid system was analyzed and optimized to produce electric energy in non-interconnected zones in the Colombian Caribbean region, contributing to the reduction of greenhouse gas emissions and the improvement in efficient energy management. A comparative analysis of the performance of hybrid was conducted using a proposed model, built with historical data for meteorological conditions, wind speed, and solar radiation. The model is integrated by a Southwest Wind Power Inc. wind turbine AIR 403, a proton-exchange membrane fuel cell (PEM), an electrolyzer, a solar panel, and a regulator based on proportional, integral, and derivative (PID) controllers to manipulate oxygen and hydrogen flow entering in the fuel cell. The transient responses of the cell voltage, current, and power were obtained for the demand of 200 W under changes in solar radiation and wind speed for each day of the year 2013 in different meteorological stations, such as Ernesto Cortissoz airport, Puerto Bolívar, Alfonso Lopez airport, and Simon Bolívar airport. Through the adjustment of the hydrogen and oxygen flow into the fuel cell, the maximum contribution of power generation from the fuel cell was presented for the Simon Bolívar airport in November with a value of 158.35 W (9.45%). Multiobjective design optimization under a Pareto diagram front is presented for each place studied to minimize the levelized cost of energy and CO₂ emission, where the objective control variables are the number of panel and stack in the photovoltaic (PV) system and PEM.

The internal components of a smart building interact through a compatible fabric and logic. A smart building integrates systems, structure, services, management, and their interrelationships to create a dynamic and cost-efficient environment. Smart buildings reduce the amount of cooling and heating load required to cool and heat spaces, thereby lowering operating costs and energy consumption without sacrificing occupant comfort. Smart structures are an Internet of Things (IoT) concern. The Internet of Things is a global network that virtualizes commonplace objects. The Internet of Things infuses non-technical objects with technology. IoT development has led to the creation of new protocols based on architectures for wireless sensor networks. Energy conservation extends the life and improves the performance of these networks, while overcoming the limitations of IoT node batteries. This research seeks to develop a data transmission model for routing IoT data in smart buildings. Utilization of intelligent object clustering and particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), and fractional chaotic order particle swarm optimization (FCPSO) optimization methods. Using the proposed algorithm to minimize energy consumption in the IoT is possible due to the algorithm’s ability to mitigate the problem by considering the number of parameters that can have a significant impact on performance, which is the goal of many optimization approaches.

Estimating the amount of solar radiation is very important in evaluating the amount of energy that can be received from the sun for the construction of solar power plants. Using machine learning tools to estimate solar energy can be a helpful method. With a high number of sunny days, Iraq has a high potential for using solar energy. This study used the Wavelet Artificial Neural Network (WANN), Wavelet Support Vector Machine (WSVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques to estimate solar energy at Wasit and Dhi Qar stations in Iraq. RMSE, EMA, R², and IA criteria were used to evaluate the performance of the techniques and compare the results with the actual measured value. The results showed that the WANN and WSVM methods had similar results in solar energy modeling. However, the results of the WANN technique were slightly better than the WSVM technique. In Wasit and Dhi Qar stations, the value of R² for the WANN and WSVM methods was 0.89 and 0.86, respectively. The value of R² in the WANN and WSVM methods in Wasit and Dhi Qar stations was 0.88 and 0.87, respectively. The ANFIS technique also obtained acceptable results. However, compared to the other two techniques, the ANFIS results were lower, and the R² value was 0.84 and 0.83 in Wasit and Dhi Qar stations, respectively.

Buildings account for sixty percent of the world’s total annual energy consumption; therefore, it is essential to find ways to reduce the amount of energy used in this sector. The road administration organization in Jakarta, Indonesia, utilized a questionnaire as well as the insights of industry experts to determine the most effective energy optimization parameters. It was decided to select variables such as the wall and ceiling materials, the number and type of windows, and the wall and ceiling insulation thickness. Several different modes were evaluated using the DesignBuilder software. Training the data with a supported vector machine (SVM) revealed the relationship between the inputs and the two critical outputs, namely the amount of energy consumption and CO₂ production, and the ant colony algorithm was used for optimization. According to the findings, the ratio of the north and east windows to the wall in one direction is 70 percent, while the ratio of the south window to the wall in the same direction ranges from 35 to 50 percent. When the ratio and percentage of the west window to the west wall is between 60 and 70 percent, the amount of produced energy and CO₂ is reduced to negligible levels.