• Energy Research

This paper proposes a novel hybrid forecasting model with three main parts to accurately forecast daily electricity prices. In the first part, where data are divided into high- and low-frequency data using the fractional wavelet transform, the best data with the highest relevancy are selected, using a feature selection algorithm. The second part is based on a nonlinear support vector network and auto-regressive integrated moving average (ARIMA) method for better training the previous values of electricity prices. The third part optimally adjusts the proposed support vector machine parameters with an error-base objective function, using the improved grey wolf and particle swarm optimization. The proposed method is applied to forecast electricity markets, and the results obtained are analyzed with the help of the criteria based on the forecast errors. The results demonstrate the high accuracy in the MAPE index of forecasting the electricity price, which is about 91% as compared to other forecasting methods.

In this paper, three different scenarios were experimentally investigated to compare carbon dioxide based enhanced oil recovery methods. These methods are continuous carbon dioxide (immiscible injection), water alternating gas, and cyclic carbon dioxide injection were investigated. In scenario A, the maximum oil recovery factor for water flooding is about 19% when there is no oil production. The maximum oil recovery at miscibility stage is about 46%. The reason for this low value of oil recovery factor might correspond to the sufficient interaction time between oil and dissolved gas. In scenario B, the total oil recovery factor is about 60% when the water alternating gas injection was performed in the system. In scenario C, after cyclic carbon dioxide injection, final oil recovery factor reached to 62%. The maximum oil recovery after miscibility stage is about 78%. In scenario B and C, regarding the more oil volume production, there are more void spaces that can be a good place for carbon dioxide storage. However, for scenario B, as the injection pattern has been changed alternatively, the void spaced had been occupied by water and this is why the carbon storage capacity was being decreased for this scenario rather than other two scenarios.

In this study, an integrated molten carbonate fuel cell (MCFC), thermoelectric generator (TEG), and regenerator energy system has been introduced and evaluated. MCFC generates power and heating load. The exit fuel gases of the MCFC is separated into three sections: the first section is transferred to the TEG to generate more electricity, the next chunk is conducted to a regenerator to boost the productivity of the suggested plant and compensate for the regenerative destructions, and the last section enters the surrounding. Computational simulation and thermodynamic evaluation of the hybrid plant are carried out utilizing MATLAB and HYSYS software, respectively. Furthermore, a thermoeconomic analysis is performed to estimate the total cost of the product and the system cost rate. The offered system is also optimized using multi-criteria genetic algorithm optimization to enhance the exergetic efficiency while reducing the total cost of the product. The power generated by MCFC and TEG is 1247.3 W and 8.37 W, respectively. The result explicates that the provided electricity and provided efficiency of the suggested plant is 1255.67 W and 38%, respectively. Exergy inquiry outcomes betokened that, exergy destruction of the MCFC and TEG is 13,945.9 kW and 262.75 kW, respectively. Furthermore, their exergy efficiency is 68.22% and 97.31%, respectively. The impacts of other parameters like working temperature and pressure, thermal conductance, the configuration of the advantage of the materials, etc., on the thermal and exergetic performance of the suggested system are also evaluated. The optimization outcomes reveal that in the final optimum solution point, the exergetic efficiency and total cost of the product s determined at 70% and 30 USD/GJ.

Supercritical carbon dioxide injection in tight reservoirs is an efficient and prominent enhanced gas recovery method, as it can be more mobilized in low-permeable reservoirs due to its molecular size. This paper aimed to perform a set of laboratory experiments to evaluate the impacts of permeability and water saturation on enhanced gas recovery, carbon dioxide storage capacity, and carbon dioxide content during supercritical carbon dioxide injection. It is observed that supercritical carbon dioxide provides a higher gas recovery increase after the gas depletion drive mechanism is carried out in low permeable core samples. This corresponds to the feasible mobilization of the supercritical carbon dioxide phase through smaller pores. The maximum gas recovery increase for core samples with 0.1 mD is about 22.5%, while gas recovery increase has lower values with the increase in permeability. It is about 19.8%, 15.3%, 12.1%, and 10.9% for core samples with 0.22, 0.36, 0.54, and 0.78 mD permeability, respectively. Moreover, higher water saturations would be a crucial factor in the gas recovery enhancement, especially in the final pore volume injection, as it can increase the supercritical carbon dioxide dissolving in water, leading to more displacement efficiency. The minimum carbon dioxide storage for 0.1 mD core samples is about 50%, while it is about 38% for tight core samples with the permeability of 0.78 mD. By decreasing water saturation from 0.65 to 0.15, less volume of supercritical carbon dioxide is involved in water, and therefore, carbon dioxide storage capacity increases. This is indicative of a proper gas displacement front in lower water saturation and higher gas recovery factor. The findings of this study can help for a better understanding of the gas production mechanism and crucial parameters that affect gas recovery from tight reservoirs.

AbstractThe information era has caused a leap in the industrial world. Industries that make full use of information technology are known as Industry 4.0, whose upstream and downstream focus on automation. However, in various aspects, there are gaps. Therefore, the use of accurate information is necessary to draw a common thread between various matters related to Industry 4.0. This paper reveals a little through the literature review as reliable information. In short, of course, it reveals the importance of understanding the changes that have occurred, a relation between the old industry and the industry 4.0.

Reactive Power Dispatch is one of the main problems in energy systems, particularly for the power industry, and a multi-objective framework should be proposed to solve it. In this study, we present a multi-objective framework for the optimization of wind turbines in wind farms. We investigate a new combined optimization method with Chaotic Local Search, Fuzzy Interactive Honey Bee Mating Optimization, Data-Sharing technique and Modified Gray Code for discrete variables. We use the proposed model to select optimal energy system parameters. The optimization process is based on simultaneous optimization of three functions. Finally, we improve a new method based on Pareto-optimal solutions to select the best one among all candidate solutions. The presented model and methodology are validated on energy systems with wind turbines. The evaluated efficiency is compared with the real system.

In this comparative study, we investigated different influential factors of mobile-assisted language learning (MALL) for improving productive and receptive language learning skills. To do this, 100 scientific research papers were selected from the top and high-quality journals based on the implications of MALL in higher education language learning. Eight papers were selected according to the specific criteria to categorize the results based on language skills interpretation and technological concepts. Therefore, after thoroughly understanding proposed methodologies and comparing them properly, underlying assumptions about this phenomenon are elaborated, and holistic and sustainable solutions are provided to address this idea. The results of this analysis indicated that Mobile devices are being utilized extensively in developing countries, with vocabulary being the primary language learning area assisted by technology and yielding satisfactory results. According to the findings of this comparative study, LINE and WhatsApp would be of interest among university students to improve receptively (listening and reading) and productivity (writing and speaking), respectively. Moreover, the technology acceptance model (TAM) would be a prominent option for teachers to adapt their current and future educational programs instead of only in-person teaching to improve students’ learning quality.