https://a-see.mazust.ac.ir/ Advances in Sustainable Energies and Environment en daily 1 Thu, 01 Jan 2026 00:00:00 +0330 Thu, 01 Jan 2026 00:00:00 +0330 https://a-see.mazust.ac.ir/article_733159.html In some areas of land planning, such as forest management, construction and development of cities, and choosing the right place to establish factories and industrial units, allocating land individually and in a scattered manner can cause problems. The approach can lead to inefficiencies and challenges in coordinating land use effectively. These problems increase the importance of the current research because the research aimed to provide a method to solve the problems. In this research, Meta Mola software has been introduced, which used metaheuristic algorithms to optimize land allocation in Gorgan Township in multi-objective problems. The main goal of this study was to provide an efficient method for contiguity and compactness land allocation using particle swarm (PSO) and firefly (FA) algorithms. By integrating metaheuristic algorithms and compactness and contiguity optimization capabilities, the software effectively modified the scattered patterns of the land and provided a more optimal allocation. One of the outstanding features of Meta Mola is the ability to use the Emgu graphical interface to identify key points and create reallocations that allow connection to GIS software. The results showed that this software, especially with an emphasis on contiguity and compactness, acts as a useful tool for land use planning with optimal performance in complex land allocation issues. The results can significantly help to reduce negative environmental effects and improve productivity in land planning. It also provides the basis for the development of sustainable development patterns. https://a-see.mazust.ac.ir/article_733169.html The CeO2-M (M: SiO2, MCM-41, MCM-48, and SBA-15) powders were fabricated by various techniques and were used as support for the Cu-based catalysts in the CO oxidation process. TPR, XRD, and N2 adsorption-desorption techniques were applied to characterize the specimens that were made. The prepared catalysts possessed high surface areas in a 121_572 m2/g span. The catalytic outcomes revealed that the Cu catalyst supported on the CeO2-SiO2 displayed the highest performance. CO conversion increased rapidly to 65% at around 150 °C and slowly reached 100% at around 300 °C. The influence of the different active species (Cu, Co, Mn, and Fe) on the optimum support was investigated, and the findings indicated that the CO conversion was considerably higher over the Co catalyst. It was observed that the activity of the Co/CeO2-SiO2 specimen rose gradually to 100% at 200 °C. A microporous and mesoporous structure with a narrow pore size distribution was also confirmed. The pretreatment tests also showed an affirmative trace of oxidative pretreatment on the catalytic efficiency. The stability tryout of the CO oxidation over the Co/CeO2-SiO2 model under dry and wet feed status during 36 h displayed high durability without any drop in CO conversion. https://a-see.mazust.ac.ir/article_733170.html The analysis and forecasting of customer electricity consumption remain among the primary challenges in the power generation industry. Over the past decades, extensive research has been conducted to enhance the accuracy and efficiency of analysis and forecasting methodologies in this domain. With the rapid progress in computer science and artificial intelligence, machine learning algorithms have emerged as robust tools for predicting customer electricity consumption, attracting significant attention from researchers. This paper proposes a hybrid method based on Harris Hawks Optimization (HHO) algorithm and Long Short-Term Memory (LSTM) neural network for load forecasting using time series data. The HHO algorithm is employed to optimize the hyperparameters of the LSTM network, including the number of LSTM units, learning rate, and number of layers. The dataset consists of electricity consumption records, weather conditions, and temporal variables from Panama for the period spanning 2015 to 2020. The evaluation, conducted using RMSE, MAPE, MAE, and MSE metrics, indicates that the proposed HHO-LSTM model outperforms conventional methods such as Support Vector Machines (SVM), linear regression, and basic neural networks. The model achieves a MAPE of 0.08% and an RMSE of 27.36. This approach offers a promising solution for optimizing energy production and distribution planning within smart power systems. https://a-see.mazust.ac.ir/article_733171.html This study explores the solvothermal synthesis and gas separation performance of composite materials composed of CuBTC, a prototypical metal–organic framework, and functionalized activated carbon (AC). The materials were prepared with varying amounts of AC to assess their effectiveness in selective CO₂/H₂ separation. Selectivity measurements were conducted using a volumetric–chromatographic approach. Structural and morphological properties of the composites were characterized by powder X-ray diffraction (XRD), nitrogen adsorption–desorption analysis, and field emission scanning electron microscopy (FESEM). The XRD data confirmed that the crystalline integrity of CuBTC remained unchanged in the presence of AC, indicating compatibility between the two components. Notably, the composite containing 0.050 g of functionalized AC exhibited a CO₂/H₂ separation factor of approximately 32, which is more than twice that of pristine CuBTC (~15) under ambient conditions (298 K, low pressure). The enhanced separation performance is attributed to carbonyl and nitro groups on the AC surface, which promote preferential CO₂ adsorption within the CuBTC–AC composite structure. https://a-see.mazust.ac.ir/article_733172.html Nowadays, food waste is disposed of in various ways considering the environmental impacts. The energy consumption for each of these methods can have environmental impacts. In this paper, the amount of pollution caused by the conversion of food waste into animal feed has been investigated. For this purpose, a reverse flow food waste dryer had been specifically constructed. Restaurant food waste was collected. Using a power analyzer, amount of kilowatt-hours consumed by the dryer at various operating levels was measured to estimate the quantities of CO₂, SO₂, and NOₓ emitted during the generation of electricity required to dry the waste using three steam power plants, a gas turbine, and a combined cycle system. The emission rates were calculated at temperatures of 55, 62.5, and 70 °C and durations of 90, 150, and 210 minutes, based on conversion factors. Among the different power plants, the steam power plant exhibited the highest environmental pollution due to its elevated production of SO₂ and CO₂. In contrast, the combined cycle system proved to be the most favorable option, generating the lowest levels of pollution. The least harmful method for converting food waste into animal feed involved producing energy with natural gas in a gas turbine power plant over a 150-minute drying period. Environmental and economic analyses revealed that operating at a temperature of 55 °C for 150 minutes resulted in the lowest environmental and economic impacts. Policymakers can use the results of economic and environmental analyses to gain a more accurate understanding of the environmental impacts of different waste management methods. https://a-see.mazust.ac.ir/article_733173.html Electric propulsion systems (EPS) are increasingly being adopted in marine industry due to their efficiency, lower greenhouse gas emissions, and operational flexibility. However, harsh marine environment and continuous operation necessitate robust condition monitoring (CM) and fault diagnosis (FD) techniques to ensure reliability and prevent catastrophic failures. Continuous monitoring detects early signs of equipment degradation, preventing unexpected failures and ensuring uninterrupted operation of electric propulsion systems. Real-time monitoring prevents hazardous conditions such as overheating, insulation breakdown, or mechanical failures, enhancing crew and vessel safety. FD techniques identify inefficiencies in motors, power electronics, and energy storage, ensuring optimal performance and reduced energy consumption. CM and FD enable condition-based maintenance, reducing unnecessary scheduled downtime and allowing repairs only when needed, optimizing operational efficiency. Therefore, this paper presents a comprehensive review of CM and FD techniques specifically developed for marine EPS, addressing both conventional approaches and cutting-edge intelligent methods. It systematically examines vibration analysis, thermal monitoring, electrical signature analysis, and lubrication monitoring as foundational CM techniques, while exploring artificial intelligence, machine learning, and digital twin technologies in fault prediction and system health management.