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Novel N-methyl-4-piperidinol (MPDL)-based solvents have been considered as high potential solvents for post combustion carbon capture, especially for power generation industry. To comprehensively investigate the CO2 absorption-regeneration performance of MPDL-based solvents, transport properties (i.e., density, viscosity, and physical CO2 diffusivity) are required. These data are reported in the literature and can be estimated by conventional predictive correlations. However, the conventional correlation is applicable for an individual solvent at various blended ratios and temperatures. Thus, artificial neural network (ANN) was then applied for prediction of the transport properties of MPDL-based solvents, including aqueous solutions of MPDL, MPDL-monoethanolamine (MEA), MPDL-2-amino-2-methyl-1-propanol (AMP), and MPDL-piperazine (PZ). Three learning algorithms of (i) Levenberg–Marquardt (LM), (ii) Bayesian Regularization (BR), and (iii) Scaled Conjugate Gradient (SCG) were applied to develop the predictive ANN models with various hidden neurons. As a result, 6 hidden neurons BR-ANN model was the most convincible single prediction platform for the three transport properties. The develop model can be very beneficial for further applications associated with the novel MPDL-based solvents.

Recently, the application of the proton exchange membrane fuel cells (PEMFCs) is extensively increasing as a popular renewable energy source. PEMFCs need low temperature for the operation along with high power density and easy implementation ability. These characteristics turned them into the most interesting type of fuel cells. However, PEMFCs need a technique for keeping them in a desired operating point. This is an essential item, especially during the current variation. In this study, a new configuration is proposed for designing an optimal PEMFC system. Two-phase interleaved boost DC/DC converter is also utilized for increasing the output voltage terminal. LQR optimal strategy is used for regulating the PEMFC. For improving the efficiency of the proposed LQR controller, a newly developed version of the whale optimization algorithm, called improved chaotic whale optimization algorithm is proposed. Simulation results of the proposed system are compared with different methods and the results showed that the proposed system has higher efficiency from the viewpoint of the current ripple and overshoot.

Limited cultivation areas in major cities have led to the possibility of vertical farming (the practice of growing crops in vertically stacked layers). However, one of the drawbacks of vertical farming is the limited availability of solar energy at the lower shelves. This study presents a model for predicting the annual sunlight availability on vertical shelves. The model uses the shelf’s structure, orientation, hourly solar radiation, and sunshine duration as inputs to Rhinoceros 3D or RHINO (a 3D computer graphics and computer-aided design application software with the Grasshopper plug-in). The calculated solar energy available at each level of the shelves from RHINO was converted to photosynthetic photon flux density (PPFD) and daily light integral (DLI) using spreadsheet software. This study investigated a vertical farm, in Chiang Mai, Thailand, with six parallel shelves at 1-meter spacing. Each shelf contained three levels with a spacing of 0.5 meters. Both north–south and east–west orientations were investigated. The model could predict PPFD and DLI at every level on the considered shelves. The north–south orientation provided uniform PPFD and DLI throughout the year. The top level of the shelves experiences the highest PPFD (1,949.86μmol*m-2*s-1) and DLI (36.80 mol*m-2*day-1). The PPFD and DLI values at the middle and bottom level were approximately 60% and 50% of the values at the top level, respectively. This information can be used for cultivation planning when considering vertical farming in urban areas. This study provides a sustainable means for future food production.

The distribution system has been constantly expanded with rising load demand due to population and economic growth. The result is a distribution line facing a voltage drop issue voltage drop, which results from increasing interconnected load and the distance from the substation. To mitigate this issue, this paper proposed an approach to improve the voltage level on the distribution line using a high-voltage capacitor bank. The system under study is modeled after the Provincial Electricity Authority (PEA) 22-kV distribution line. The placement of the high voltage capacitor bank will be based on the 2/3 rule technique. The PSCAD/EMTDC has been used to simulate the characteristics of the case study distribution system and evaluate the performance of the proposed technique in comparison with the PEA voltage regulation standard. The result has demonstrated the ability to use the proposed high voltage capacitor bank placement technique to improve voltage levels in the distribution system.

The flexible operation of battery energy storage systems (BESS) to support electricity grid modernization requires optimal planning and an efficient control strategy. This paper proposes the optimal allocation of BESS with photovoltaic systems for microgrids to enhance grid reliability and flexibility. An adaptive BESS control strategy is developed to allow the BESS to operate in both redundant and peak shaving modes. An evolutionary programming-based genetic algorithm implemented with an optimal power flow control is proposed to determine the BESS’s optimal location, sizing, and charge/discharge operation. The objective function is to maximize the benefits of energy loss reduction and peak shaving enhancement while minimizing the installation cost. The proposed approach is conducted with MATLAB and DIgSILENT PowerFactory software, which uses an intelligent automatic data exchange process to solve the optimal solution. A practical 22 kV grid-connected microgrid of Thailand is used as a test system to demonstrate the effectiveness of the planning and control strategy for the BESS installation. The simulation test results show that the proposed optimal BESS allocation and control strategy can significantly reduce the microgrid’s energy loss and peak demand and increase energy shaving leading to enhanced grid resiliency and reliability.

The climate crisis has become a significant concern for many worldwide sectors. One of the primary causes of global warming comes from carbon dioxide (CO2) emissions, in which the transport sector is the major emitter, especially from road vehicles. Electric vehicles (EVs) have been seen as the best decarbonization choice to respond to the global warming issue by replacing fossil-fueled engines with electrically driven engines, especially electricity from renewable energy sources. Last year, Thailand’s government unveiled a roadmap with a view to achieving EV production of at least 30% of all auto production in the country by 2030 through the 30@30 policy plan. This study describes the potential reductions of CO2 emissions and their cost resulting from the transition to EVs in Thailand. The CO2 emissions of EVs caused by Thailand’s road vehicle fleet and battery production have been considered, and the forecasting scenarios of the proportion of EVs in 2030 have been studied. In the scenarios, the number of road cars in 2030 was estimated based on the registered vehicle statistics in Thailand. The data on seven-passenger cars powered by gasoline and electricity are used. A potential incentive from the government to encourage the use of EVs is also discussed based on analyzing the cost of carbon.

The performance of chemically modified textile waste for a heavy metal treatment is reported in this study. Two sorbents, which were prepared by a simple and concise method, were able to bind two heavy metals including Pb(II) and Cr(VI), with very high efficiencies. The binding mechanisms were studied through adsorption tests such as the influence of contact times, heavy metal concentrations as well as elution study. The overall results showed that the adsorption kinetics was very fast and attained an equilibrium within 180 min in all metals studied. The maximum single metal uptakes were 11.81 mg/g and 1.97 mg/g, for Pb(II) and Cr(VI), respectively. The sorbent could potentially be effective in treating large volumes of wastewater. Keywords: Modified textile, Textile waste, Chemisorption, Graft copolymerization, Amino chelating, Ion exchange

Climate change is the biggest 21st-century environmental challenge that impacts human communities, natural resources, and biodiversity. This study aims to study the economic and energy impacts on climate change measured by greenhouse gas emissions in China and the USA. Various factors are considered in this study; thus, the traditional regression analysis (OLS regression) may not be practical when the number of predictors is large, and multicollinearity exists. We suggest using three machine learning models, namely LASSO regression, Ridge regression, and Elastic net regression to deal with these limitations of the OLS method. Our results show that the impacts of economic factors for China and the USA. are slightly different. Chinese economic factors are found to increase greenhouse gas emissions, while there is a decrease in greenhouse gas emissions in the USA. However, we find strong evidence that renewable energy production leads to sustainable development in both the USA. and China.