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The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.

The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.

Nanofluid is a new class of engineering fluid that has good heat transfer characteristics which is essential to increase the heat transfer performance in various engineering applications such as heat exchangers and cooling of electronics. In this study, experiments were conducted to compare the heat transfer performance and pressure drop characteristics in a plate heat exchanger (PHE) for 30° and 60° chevron angles using water based Al2O3 nanofluid at the concentrations from 0 to 0.5 vol.% for different Reynolds numbers. The thermo-physical properties has been determined and presented in this paper. At 0.5 vol% concentration, the maximum heat transfer coefficient, the overall heat transfer coefficient and the heat transfer rate for 60° chevron angle have attained a higher percentage of 15.14%, 7.8% and 15.4%, respectively in comparison with the base fluid. Consequently, when the volume concentration or Reynolds number increases, the heat transfer coefficient and the overall heat transfer coefficient as well as the heat transfer rate of the PHE (Plate Heat Exchangers) increases respectively. Similarly, the pressure drop increases with the volume concentration. 60° chevron angle showed better performance in comparison with 30° chevron angle.

Nanofluid is a new class of engineering fluid that has good heat transfer characteristics which is essential to increase the heat transfer performance in various engineering applications such as heat exchangers and cooling of electronics. In this study, experiments were conducted to compare the heat transfer performance and pressure drop characteristics in a plate heat exchanger (PHE) for 30° and 60° chevron angles using water based Al2O3 nanofluid at the concentrations from 0 to 0.5 vol.% for different Reynolds numbers. The thermo-physical properties has been determined and presented in this paper. At 0.5 vol% concentration, the maximum heat transfer coefficient, the overall heat transfer coefficient and the heat transfer rate for 60° chevron angle have attained a higher percentage of 15.14%, 7.8% and 15.4%, respectively in comparison with the base fluid. Consequently, when the volume concentration or Reynolds number increases, the heat transfer coefficient and the overall heat transfer coefficient as well as the heat transfer rate of the PHE (Plate Heat Exchangers) increases respectively. Similarly, the pressure drop increases with the volume concentration. 60° chevron angle showed better performance in comparison with 30° chevron angle.

The relationship between exchange rate volatility (ERV) and other macroeconomic factors, including trade flows, domestic production, inflation, money demand, and economic growth, has remained a topic of a large number of studies in international finance. However, the research question that the past empirics have overlooked is whether ERV has any role in helping an emerging economy like China in its journey toward a renewable energy transition. To answer this question, this analysis intends to scrutinize the nexus between ERV and renewable energy investment (REI) in China over 1991Q1-2021Q4. Moreover, due to the asymmetric behavior of ERV, we have based our analysis on the asymmetric assumption. For analyzing the short and long-run impacts of ERV on REI in China, we used the linear autoregressive distributed lag (ARDL) and nonlinear ARDL methods. In the long term, the ARDL model predicts that stricter environmental policies and higher GDP will lead to more investment in renewable energy. As far as the nonlinear model is concerned, the long-term negative change in ERV does not affect REIs, whereas a long-term positive change in ERV reduces such investments. Likewise, unfavorable short-term exchange rate shocks encourage REI while positive short-term shocks discourage such development. Moreover, investment in renewable energy is bolstered by GDP, environmental policy strictness, and financial development, but is dampened by CO2 emissions in the short term in both models. On the basis of these results, we suggest that policymakers should consider implementing measures to stabilize exchange rates to promote investment in renewable energy.

The relationship between exchange rate volatility (ERV) and other macroeconomic factors, including trade flows, domestic production, inflation, money demand, and economic growth, has remained a topic of a large number of studies in international finance. However, the research question that the past empirics have overlooked is whether ERV has any role in helping an emerging economy like China in its journey toward a renewable energy transition. To answer this question, this analysis intends to scrutinize the nexus between ERV and renewable energy investment (REI) in China over 1991Q1-2021Q4. Moreover, due to the asymmetric behavior of ERV, we have based our analysis on the asymmetric assumption. For analyzing the short and long-run impacts of ERV on REI in China, we used the linear autoregressive distributed lag (ARDL) and nonlinear ARDL methods. In the long term, the ARDL model predicts that stricter environmental policies and higher GDP will lead to more investment in renewable energy. As far as the nonlinear model is concerned, the long-term negative change in ERV does not affect REIs, whereas a long-term positive change in ERV reduces such investments. Likewise, unfavorable short-term exchange rate shocks encourage REI while positive short-term shocks discourage such development. Moreover, investment in renewable energy is bolstered by GDP, environmental policy strictness, and financial development, but is dampened by CO2 emissions in the short term in both models. On the basis of these results, we suggest that policymakers should consider implementing measures to stabilize exchange rates to promote investment in renewable energy.

Agriculture is the largest sector of Pakistan’s economy, contributing almost 22% to the GDP and employing almost 45% of the total labor force. The two largest food crops, wheat and rice, contribute 3.1% and 1.4% to the GDP, respectively. The objective of this research was to calculate the energy return on investment (EROI) of these crops on a national scale from 1999 to 2009 to understand the size of various energy inputs and to discuss their contributions to the energy output. Energy inputs accounted for within the cropping systems included seed, fertilizer, pesticide, human labor, tractor diesel, irrigation pump electricity and diesel, the transport of fertilizer and pesticide, and the embodied energy of tractors and irrigation pumps. The largest per-hectare energy inputs to wheat were nitrogen fertilizer (52.6%), seed (17.9%), and tractor diesel (9.1%). For rice, the largest per-hectare energy inputs were nitrogen fertilizer (32%), tube well diesel (19.8%), and pesticide (17.6%). The EROI of wheat showed a gradual downward trend between 2000 and 2006 of 21.3%. The trend was erratic thereafter. Overall, it ranged from 2.7 to 3.4 with an average of 2.9 over the 11-year study period. The overall trend was fairly consistent compared to that of rice which ranged between 3.1 and 4.9, and averaged 3.9. Rice’s EROI dipped sharply in 2002, was erratic, and remained below four until 2007. It rose sharply after that. As energy inputs increased, wheat outputs increased, but rice outputs decreased slightly. Rice responded to inputs with greater output and an increase in EROI. The same was not true for wheat, which showed little change in EROI in the face of increasing inputs. This suggests that additional investments of energy in rice production are not improving yields but for wheat, these investments are still generating benefits. The analysis shows quantitatively how fossil energy is a key driver of the Pakistani agricultural system as it traces direct and indirect energy inputs to two major food crops.

Agriculture is the largest sector of Pakistan’s economy, contributing almost 22% to the GDP and employing almost 45% of the total labor force. The two largest food crops, wheat and rice, contribute 3.1% and 1.4% to the GDP, respectively. The objective of this research was to calculate the energy return on investment (EROI) of these crops on a national scale from 1999 to 2009 to understand the size of various energy inputs and to discuss their contributions to the energy output. Energy inputs accounted for within the cropping systems included seed, fertilizer, pesticide, human labor, tractor diesel, irrigation pump electricity and diesel, the transport of fertilizer and pesticide, and the embodied energy of tractors and irrigation pumps. The largest per-hectare energy inputs to wheat were nitrogen fertilizer (52.6%), seed (17.9%), and tractor diesel (9.1%). For rice, the largest per-hectare energy inputs were nitrogen fertilizer (32%), tube well diesel (19.8%), and pesticide (17.6%). The EROI of wheat showed a gradual downward trend between 2000 and 2006 of 21.3%. The trend was erratic thereafter. Overall, it ranged from 2.7 to 3.4 with an average of 2.9 over the 11-year study period. The overall trend was fairly consistent compared to that of rice which ranged between 3.1 and 4.9, and averaged 3.9. Rice’s EROI dipped sharply in 2002, was erratic, and remained below four until 2007. It rose sharply after that. As energy inputs increased, wheat outputs increased, but rice outputs decreased slightly. Rice responded to inputs with greater output and an increase in EROI. The same was not true for wheat, which showed little change in EROI in the face of increasing inputs. This suggests that additional investments of energy in rice production are not improving yields but for wheat, these investments are still generating benefits. The analysis shows quantitatively how fossil energy is a key driver of the Pakistani agricultural system as it traces direct and indirect energy inputs to two major food crops.