• Energy Research
• 2016-2025close
• 12. Responsible consumption close
• 6. Clean water close
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The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

It has been known for a very long time that chemical energy may be converted into electrical energy by using biomass, considered a renewable energy source. In the study that is being presented here, an explanation and a presentation are offered on a one-of-a-kind hybrid system that generates dependable power and cooling by harnessing the chemical energy of biomass. An anaerobic digester takes in organic material and converts it into biomass by using the high-energy content of cow manure as fuel. The Rankin cycle is the primary engine that drives the system that produces energy, and its combustion-based byproducts are routed to an ammonia absorption refrigeration system in order to provide sufficient cooling for the process of pasteurizing and drying the milk. It is expected that solar panels might contribute to the production of sufficient amounts of power for necessary activities. The technical and financial facets of the system are both being investigated at the moment. In addition, the optimal working conditions are determined by employing a forward-thinking multi-objective optimization strategy. This method simultaneously raises the operational effectiveness to the greatest extent that is practically possible while simultaneously lowering both expenses and emissions. The findings indicate that under ideal conditions, the levelized cost of the product (LCOP), efficiency, and emission of the system are, respectively, 0.087 $/kWh, 38.2%, and 0.249 kg/kWh. The digester and the combustion chamber both have very high exergy destruction rates, with the digester having the highest rate and the combustion chamber having the second-highest rate among all of the system's components. This assertion is supported by every one of these components.

It has been known for a very long time that chemical energy may be converted into electrical energy by using biomass, considered a renewable energy source. In the study that is being presented here, an explanation and a presentation are offered on a one-of-a-kind hybrid system that generates dependable power and cooling by harnessing the chemical energy of biomass. An anaerobic digester takes in organic material and converts it into biomass by using the high-energy content of cow manure as fuel. The Rankin cycle is the primary engine that drives the system that produces energy, and its combustion-based byproducts are routed to an ammonia absorption refrigeration system in order to provide sufficient cooling for the process of pasteurizing and drying the milk. It is expected that solar panels might contribute to the production of sufficient amounts of power for necessary activities. The technical and financial facets of the system are both being investigated at the moment. In addition, the optimal working conditions are determined by employing a forward-thinking multi-objective optimization strategy. This method simultaneously raises the operational effectiveness to the greatest extent that is practically possible while simultaneously lowering both expenses and emissions. The findings indicate that under ideal conditions, the levelized cost of the product (LCOP), efficiency, and emission of the system are, respectively, 0.087 $/kWh, 38.2%, and 0.249 kg/kWh. The digester and the combustion chamber both have very high exergy destruction rates, with the digester having the highest rate and the combustion chamber having the second-highest rate among all of the system's components. This assertion is supported by every one of these components.

This study reports the fabrication of a new cathode electrode assembly using polyaniline (PANI) and graphene on a stainless steel mesh (SSM) as an alternative for the conventional expensive cathode of microbial electrolysis cells (MECs). With respect to the previous efforts to propose an efficient and cost-effective alternative for platinum (Pt) catalysts and cathode electrodes, the present study investigates the assessment of different catalysts to elucidate the potential of the modified SSM cathode electrode for larger-scale MECs. In the case of feeding dairy wastewater to the MEC, the maximum hydrogen production rate and COD removal were obtained by SSM/PANI/graphene cathode and had the values 0.805 m3 H2 m-3 anolyte day-1 and 82%, respectively, at the applied potential of 1 V. These values were only 20% and 7% lower than those of the MEC with Pt on the carbon cloth cathode, respectively. The coulombic efficiencies of SSM/Pt and SSM/PANI/graphene were seen to be 64.48% and 56.67%, respectively. It was also concluded that the fabrication cost of the modified cathode was 50% lower than the conventional cathodes with Pt on the carbon cloth. Finally, the evaluation of the modified cathode performance was achieved based on Fourier transform infrared spectroscopy, linear sweep voltammetry, scanning electron microscopy, and atomic force microscopy.

This study reports the fabrication of a new cathode electrode assembly using polyaniline (PANI) and graphene on a stainless steel mesh (SSM) as an alternative for the conventional expensive cathode of microbial electrolysis cells (MECs). With respect to the previous efforts to propose an efficient and cost-effective alternative for platinum (Pt) catalysts and cathode electrodes, the present study investigates the assessment of different catalysts to elucidate the potential of the modified SSM cathode electrode for larger-scale MECs. In the case of feeding dairy wastewater to the MEC, the maximum hydrogen production rate and COD removal were obtained by SSM/PANI/graphene cathode and had the values 0.805 m3 H2 m-3 anolyte day-1 and 82%, respectively, at the applied potential of 1 V. These values were only 20% and 7% lower than those of the MEC with Pt on the carbon cloth cathode, respectively. The coulombic efficiencies of SSM/Pt and SSM/PANI/graphene were seen to be 64.48% and 56.67%, respectively. It was also concluded that the fabrication cost of the modified cathode was 50% lower than the conventional cathodes with Pt on the carbon cloth. Finally, the evaluation of the modified cathode performance was achieved based on Fourier transform infrared spectroscopy, linear sweep voltammetry, scanning electron microscopy, and atomic force microscopy.

Abstract Sustainable production of biofuels has provided an attractive alternative to fossil fuels, which has relieved the concern regarding energy supply and global climate change. Currently, interest in metabolic engineering of yeasts as microbial cell factories for biofuel production, which varies from short-chain ethanol to long-chain fatty acid-derived molecules, is growing. The commercial production of new energy-dense biofuels using yeasts and new synthetic biology tools is now possible due to recent developments in metabolic engineering. Here, it is attempted to comprehensively and critically review the latest advances in metabolism-targeted strategies and the production of different types of biofuels using yeasts. Furthermore, the key challenges and perspectives have been discussed for improving yeast biorefineries for the production of biofuels, such as host compatibility of heterologous genes, substrate extension for alternative feedstocks, better tools for reprogramming cell metabolism, host robustness for tolerating or alleviating toxicity induced by end products, and new design principles with predictable behaviors for the constructed biological systems.