Waste management is becoming one of the most challenging tasks for developing countries in order to ensure good human health, as well as a healthy environment. Rajshahi City Corporation (RCC) is one of the 12 city corporations in Bangladesh. Various environmental and human health problems have arisen due to a lack of proper knowledge of waste management. Thus, the aim of this work is to illustrate the present status of MSW generation and management in Rajshahi City Corporation, Bangladesh. Fifty households were selected throughout RCC for waste collection, which represent approximately all types of households in RCC. From the qualitative and quantitative analysis, it is estimated that the approximate MSW generation in RCC is 358.19 t/d (tons/day) at an approximate rate of 0.4214 kg/person/d. Calorific values of the wastes have also been determined using the ultimate analysis results of the MSW. A higher calorific value of the dry MSW has been calculated as 14.9 MJ/kg. Moisture content of the MSW has been found to be 48.28%. It is also estimated that the possible power generation (steam energy to electrical power) from MSW generated in RCC is 159.40 MWh/d. Lastly, future scopes of MSW management and different waste management measures that need to be taken are illustrated. Waste-to-energy (WTE) conversion has been given priority and anaerobic digestion (AD) has been found to be an interesting prospect in this sector. Techno-economic analysis of the AD has been conducted. Energy potential from the proposed plant has been calculated as 3.85 MW and the payback period has been found to be 4.9 years. It has been observed that employing AD on a large scale can not only reduce the waste, but also meet a large portion of the energy demand of this city.

The aims of this paper are to develop hybrid energy systems considering biomass energy sources as well as a framework and optimal configuration of hybrid systems of energy for a southern sub-urban area of Bhola district in Bangladesh, named Kukri Mukri island, and analyse the feasibility of the techno-economic prospects of these systems. In this work, electrification for the rural area is analysed for different configurations of the hybrid systems. The estimation of available resources with optimal sizing and analysis of techno-economic aspects is done through HOMER Pro software to satisfy the demand of peak load. Different configurations of hybrid energy systems, including PV/diesel, PV/wind, PV/diesel/wind, PV/wind/diesel/biomass, and wind/diesel, are analysed and compared through optimization of different energy sources in HOMER. The size of the system and components are optimized and designed depending on the net present cost (NPC) and the levelized cost of energy (LCOE). Due to the lower availability and rising cost of wind energy, the outcome of this work shows a solar-based photovoltaic (PV) as the main energy source, battery as the storage media, and diesel generator as an energy source for backup. The results indicate that LCOE is much lower for PV/wind/diesel/biomass (0.142 USD/kWh) than PV/diesel (0.199 USD/kWh), PV/wind (0.239 USD/kWh), PV/diesel/wind (0.167 USD/kWh), PV/diesel (0.343 USD/kWh), and wind/diesel (0.175 USD/kWh). Additionally, it is demonstrated from the research that the genetic algorithm (GA) process gives sustainable and cost-effective outcomes compared to HOMER.

Synthetic gas generated from the gasification of biomass feedstocks is one of the clean and sustainable energy sources. In this work, a fixed-bed downdraft gasifier was used to perform the gasification on a lab-scale of rice husk, sawdust, and coconut shell. The aim of this work is to find and compare the synthetic gas generation characteristics and prospects of sawdust and coconut shell with rice husk. A temperature range of 650–900 °C was used to conduct gasification of these three biomass feedstocks. The feed rate of rice husk, sawdust, and coconut shell was 3–5 kg/h, while the airflow rate was 2–3 m³/h. Experimental results show that the highest generated quantity of methane (vol.%) in synthetic gas was achieved by using coconut shell than sawdust and rice husk. It also shows that hydrogen production was higher in the gasification of coconut shell than sawdust and rice husk. In addition, emission generations in coconut shell gasification are lower than rice husk although emissions of rice husk gasification are even lower than fossil fuel. Rice husk, sawdust, and coconut shell are cost-effective biomass sources in Bangladesh. Therefore, the outcomes of this paper can be used to provide clean and economic energy sources for the near future.

The bearing is an essential component of a rotating machine. Sudden failure of the bearing may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault diagnosis technique was developed to diagnose various faults that occur in a deep groove ball bearing. An experimental setup was designed and developed to generate faulty data in various conditions, such as inner race fault, outer race fault, and cage fault, along with the healthy condition. The time waveform of raw vibration data generated from the system was transformed into a frequency spectrum using the fast Fourier transform (FFT) method. These FFT signals were analyzed to detect the defective bearing. Another significant contribution of this paper is the application of a machine learning (ML) algorithm to diagnose bearing faults. The support vector machine (SVM) was used as the primary algorithm. As the efficiency of SVM heavily depends on hyperparameter tuning and optimum feature selection, the particle swarm optimization (PSO) technique was used to improve the model performance. The classification accuracy obtained using SVM with a traditional grid search cross-validation (CV) optimizer was 92%, whereas the improved accuracy using the PSO-based SVM was found to be 93.9%. The developed model was also compared with other traditional ML techniques such as k-nearest neighbor (KNN), decision tree (DT), and linear discriminant analysis (LDA). In every case, the proposed model outperformed the existing algorithms.

One of the biggest issues impeding a country’s progress is the lack of power. To overcome this issue, hybrid renewable energy systems (HRES) play an important role. Due to rising consumption and diminishing resources globally, sustainability has recently attracted more attention. Bangladesh has access to renewable energy sources, including solar, micro-hydro, biomass, wind, and others. The objective of this research is to minimize the net present cost (NPC), cost of energy (COE), and CO₂ emissions of the suggested electricity network using the Hybrid Optimization Model for Multiple Energy Resources (HOMER) Pro Software. This investigation explores the possible use of a hybridized energy system (i.e., solar, wind, and diesel) with battery storage in Bangladesh’s northern area. Utilizing HOMER Pro software, an optimal grid-connected system is chosen after evaluating the techno-economic viability of several configuration options. For the Rangpur metropolitan region, seven distinct grid-connected solutions with stationary renewable sources are simulated. The HRES is designed to meet demands for hospital, diagnostic, school, and operation theatre loads of 3250.00 kWh, 250.00 kW maximum requirement, and 570.00 kWh, 71.25 kW maximum electricity demand, respectively. Multivariate linear regression (MLR) is used to assess the suggested optimal combination in terms of system size, cost, technical performance, and environmental stability. The findings show that the metric real-time rate (annual) has emerged as the most advantageous option since economic criteria like total NPC and COE are preferred above others.

Rotating machines require bearings to operate smoothly. However, wear, misalignment, and poor lubrication can degrade bearings over time. Fault diagnosis models identify and classify bearing faults. A fault diagnosis model trained in a specific working condition may not perform well in different working conditions. Real-world datasets are mixed with various work environment conditions; therefore, validating a model using different working conditions datasets is necessary. In this study, raw vibrational accelerometer data of variable working conditions is preprocessed using the window length and stride method to generate a data format suitable for evaluating the proposed model. This model employs the Transfer learning-based VGG16 model as the feature extractor and random forest as the classifier, and it has proven to be highly effective. This proposed fault diagnosis model adapts to different work environments and enhances fault classification at variable working conditions. The performance of the proposed model is evaluated using various metrics such as confusion matrix heatmap, t-SNE plot, precision-recall curve and learning curve. Results obtained from these metrics indicate that this model performs well compared to others. The overall accuracy of the model is 99.90%, and both the training and testing of this model are fast. It is evident from the learning curve evaluation that this model is free from over- or under-fitting issues. Overall, this model is reliable and suitable for classifying bearing faults at different working conditions and can be useable for real world purposes.