This paper proposes an improved biogeography-based optimization (BBO) algorithm to effectively solve the economic and environmental dispatch (EED) problem in power systems. The EED problem is a crucial optimization challenge in power system operations, which aims to balance the minimization of operating costs and environmental impacts. Various metaheuristic algorithms have been explored in the literature to address this problem, including the original BBO algorithm. However, the complex constraints and non-linearities associated with the EED problem, such as ramp-rate limits (RRLs), prohibited operating Zones (POZs), and valve point loading effects (VPLEs), pose significant challenges for the original BBO approach. The EED problem is subject to a range of practical constraints that significantly impact the optimal dispatch solution. Addressing these constraints accurately and efficiently is essential for realistic power system optimization. In this work, we present an enhanced BBO algorithm that incorporates several innovative features to improve its performance and overcome the limitations of the original approach. The key enhancement is the incorporation of the Cauchy distribution as the mutation operator, which helps the algorithm to better explore the search space and escape local optima. Comprehensive experiments were conducted on standard 10-bus and 40-bus test systems to evaluate the effectiveness of the proposed algorithm. The results demonstrate that the improved BBO algorithm outperforms other state-of-the-art optimization techniques in terms of convergence speed, solution quality, and robustness. Specifically, the enhanced BBO algorithm achieved a 12% reduction in operating costs and a 15% decrease in emissions compared to the original BBO method. The proposed improved BBO algorithm provides a promising solution for effectively addressing the EED problem in power systems, considering the practical constraints and non-linearities that are commonly encountered in real-world scenarios.

In today’s supply chain management, there is a growing emphasis on transitioning to environmentally sustainable practices. This paper aimed to identify and rank the barriers to the implementation of eco-regenerative supply chains. A novel integrated approach was proposed based on stepwise weighted assessment ratio analysis (SWARA) and the multi-attributive border approximation area (MABAC) method using ZE-fuzzy numbers. This approach aimed to address some of the limitations of the failure mode and effects analysis (FMEA) method, including lack of thorough prioritization and inability to make decisions about the importance of various failure factors in an uncertain environment. By combining fuzzy sets and considering the reliability levels of two distinct groups of decision-makers and experts, this proposed method offers a comprehensive evaluation framework. Following the determination of the risk priority number (RPN) by the FMEA method, risk factors were evaluated using ZE-SWARA, and barriers were ranked using the ZE-MABAC method to identify critical barriers and propose corrective actions. Furthermore, sensitivity analysis was conducted in this study to demonstrate the viability of the proposed method. This research contributes to the advancement of eco-regenerative supply chain management practices by offering a systematic and innovative approach to addressing environmental concerns and improving decision-making processes in uncertain environments.

Lockdowns were implemented in nearly all countries in the world in order to reduce the spread of COVID-19. The majority of the production activities like industries, transportation, and construction were restricted completely. This unprecedented stagnation of resident’s consumption and industrial production has efficiently reduced air pollution emissions, providing typical and natural test sites to estimate the effects of human activity controlling on air pollution control and reduction. Air pollutants impose higher risks on the health of human beings and also damage the ecosystem. Previous research has used machine learning (ML) and statistical modeling to categorize and predict air pollution. This study developed a binary spring search optimization with hybrid deep learning (BSSO-HDL) for air pollution prediction and an air quality index (AQI) classification process during the pandemic. At the initial stage, the BSSO-HDL model pre-processes the actual air quality data and makes it compatible for further processing. In the presented BSSO-HDL model, an HDL-based air quality prediction and AQI classification model was applied in which the HDL was derived by the use of a convolutional neural network with an extreme learning machine (CNN-ELM) algorithm. To optimally modify the hyperparameter values of the BSSO-HDL model, the BSSO algorithm-based hyperparameter tuning procedure gets executed. The experimental outcome demonstrates the promising prediction classification performance of the BSSO-HDL model. This model, developed on the Python platform, was evaluated using the coefficient of determinationR², the mean absolute error (MAE), and the root mean squared error (RMSE) error measures. With an R² of 0.922, RMSE of 15.422, and MAE of 10.029, the suggested BSSO-HDL technique outperforms established models such as XGBoost, support vector machines (SVM), random forest (RF), and the ensemble model (EM). This demonstrates its ability in providing precise and reliable AQI predictions.

A cellulose-based aerogel has been synthesized from rambutan peel to mitigate environmental pollution caused by agricultural waste, rendering it an eco-friendly material with potential applications in oil spill remediation as well as enhancing the value of this fruit. The objective of this study was to extract cellulose from rambutan peel using chlorination and alkalization processes, followed by optimizing the synthesis conditions of cellulose-based aerogels from rambutan peel through experimental designs to improve oil removal efficiency. In this research, cellulose-based aerogel material was synthesized using the sol-gel method, utilizing waste from rambutan peel as the substrate and polyvinyl alcohol as the cross-linking agent, followed by freeze-drying. A central composite design with 30 different experimental setups was employed to investigate the influence of cellulose content (1.0–2.0%), cross-linking agent (polyvinyl alcohol) content (0.1–0.3%), ultrasonic time (5–15 min), and ultrasonic power (100–300W) on the oil adsorption capacity (g/g) of cellulose-based aerogels from rambutan peel. The research findings demonstrated successful extraction of cellulose from rambutan peel through chlorination, followed by softening with 17.5% (w/v) sodium hydroxide. Response surface plots indicated that maximizing the cellulose component could lead to a maximum diesel oil adsorption capacity of up to 52.301 g/g. Cellulose-based aerogel exhibits ultra-lightweight properties (0.027±0.002 g/cm³), high porosity (97.88±0.19), hydrophobicity (water contact angle of 152.7^o), and superior oil selective adsorption compared to several commercially available materials in the market, demonstrating promising potential for application in treating oil-contaminated water in real-world scenarios.

This study is focused on the use of random forest (RF) to forecast the streamflow in the Kesinga River basin. A total of 169 data points were gathered monthly for the years 1991–2004 to create a model for streamflow prediction. The dataset was allotted into training and testing stages using various ratios, such as 50/50, 60/40, 70/30, and 80/20. The produced models were evaluated using three statistical indices: the root mean square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (CC). The analysis of the models' performances revealed that the training and testing ratios had a substantial impact on the RF model's predictive abilities; models performed best when the ratio was 60/40. The findings demonstrated the right dataset ratios for precise streamflow prediction, which will be beneficial for hydraulic engineers during the water-related design and engineering stages of water projects.

We consider a resource extraction model with the possibility of extending the resource stock by drilling efforts and an oligopolistic competition of symmetric firms. Assuming a hyperbolic market demand, we adopt the open-loop Nash equilibrium to analyze and compare the outcomes of a private vs a common resource pool. For both cases, the drilling efforts of the individual firms strongly depend on the market structure. For a low competition, the efforts increase the number of firms, and the opposite is true for a high competition. On the other hand, aggregate drilling efforts are different among the two types of pools and are opposite to Schumpeterian’s hypothesis (i.e., they are either an inverted U-shaped (private pools) or strictly increasing (single pools)).

Short-term reductions in social welfare, expected to be caused by a currently imposed carbon tax, are among the obstacles to a rapid transition to a low-carbon economy. Using an extended DICE model, we studied the potential of green bonds to both accelerate this transition and smoothen welfare losses and gains in a socially optimal way. We showed that green bonds can indeed accelerate the transition to a low-carbon economy and that lower interest rates on bonds speed up this acceleration. Moreover, bonds can reduce short-term welfare losses; however, to eliminate welfare losses, additional compensation mechanisms are needed. For example, bonds at a 3% interest rate can decrease the peak atmospheric carbon concentrations by about 20% and shorten the initial time, during which society is worse off from 75 to 45 years. Retaining at least the same consumption level as in the no-mitigation scenario, without using bonds, is possible only through a decrease in abatement efforts. Green bonds of sufficiently low interest rates allow improving intertemporal welfare as well as achieving a more pronounced climate change mitigation with respect to both mitigation and no-mitigation scenarios without bonds.

Predicting river water quality in the Special Region of Yogyakarta (DIY) is crucial. In this research, we modeled a river water quality prediction system using the artificial neural network (ANN) backpropagation method. Backpropagation is one of the developments of the multilayer perceptron (MLP) network, which can reduce the level of prediction error by adjusting the weights based on the difference in output and the desired target. Water quality parameters included biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), dissolved oxygen (DO), total phosphate, fecal coliforms, and total coliforms. The research object was the upstream, downstream, and middle parts of the Oya River. The data source was secondary data from the DIY Environment and Forestry Service. Data were in the form of time series data for 2013–2023. Descriptive data results showed that the water quality of the Oya River in 2020–2023 was better than in previous years. However, increasing community and industrial activities can reduce water quality. This was concluded based on the prediction results of the ANN backpropagation method with a hidden layer number of 4. The prediction results for period 3 in 2023 and period 1 in 2024 are that 1) the concentrations of BOD, fecal coli, and total coli will increase and exceed quality standards, 2) COD and TSS concentrations will increase but will still be below quality standards, 3) DO and total phosphate concentrations will remain constant and still on the threshold of quality standards. The possibility of several water quality parameters increasing above the quality standards remains, so the potential for contamination of the Oya River is still high. Therefore, early prevention of river water pollution is necessary.