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Abstract Dimethyl ether appears to be a better choice among various diesel alternatives due to its high cetane number and sootless combustion. However, the physical and chemical properties of dimethyl ether are very different from those of diesel. The physical properties influence spray formation and atomization characteristics, while chemical properties determine combustion and emission formation characteristics. Thus, fuel's physical and chemical properties significantly determine engine performance and emissions. In the present work, spray combustion and emission formation characteristics of n-heptane, dimethyl ether, and their blends (10, 25, and 50% dimethyl ether in n-heptane) were numerically studied in a constant volume chamber. Results show that the n-heptane spray combustion has the highest heat release rate with an intense premix combustion phase, whereas dimethyl ether spray combustion has the lowest heat release rate and shortest premix combustion phase. The magnitude of the premixed phase and heat release rate decreases with the increase in dimethyl ether mass fraction in the blends. Soot, carbon monoxide (CO), unburned hydrocarbon (UHC), and nitric oxide (NO) emissions decreased with the increase in the dimethyl ether mass fraction in the blends and were lowest for the dimethyl ether.

Abstract This study investigates the non-linear relationship between urbanization paths and CO2 emissions in selected South, South-East, and East Asian countries over the period 1971–2014. Based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework, we applied the advanced and robust methods of dynamic seemingly unrelated regression (DSUR), dynamic OLS (DOLS), and fully modified OLS (FMOLS) to estimate the long-term effects. The empirical findings revealed the inverted U-shaped effects of urbanization and urban agglomeration and the U-shaped impact of the largest city ratio on CO2 emissions. Urbanization and urban agglomerations improve environmental quality in the long-run and support ecological modernization theory. However, excessive concentration in the largest cities have severely affected the environmental quality and violates the notion of compact-city efficiencies. Moreover, energy intensity and economic growth positively affect CO2 emissions, while trade openness negatively influences CO2 emissions. Our robustness analysis at the country-level applies the augmented mean group (AMG) panel ARDL technique, which further supports the non-linear effect of urbanization paths on CO2 emissions except for a few countries. The results of the panel Granger non-causality approach unveil bidirectional causality of energy efficiency, economic growth, urbanization, and largest city ratio with CO2 emissions. In contrast, unidirectional causality runs from urban agglomeration to CO2 emissions. Our findings have important policy implications as we suggest green urban infrastructures, eco-friendly dwellings, smart cities, country-specific trade policies, and renewable energy options to improve the environmental quality.

Abstract Double and added double stage organic Rankine cycle systems are configured to recover exhaust gas waste heat of dual fuel engines. To evaluate the performance of the models proposed here, energy, exergy and economic analyses are performed. Several working fluids are evaluated for recommendation for double and added double stage organic Rankine cycle systems. In the double stage organic Rankine cycle, cycle 1 and cycle 2 are connected in parallel. Working fluids R123, R141b, and R601 are used in cycle 1, and R245fa, R236ea, and R1233zd in cycle 2. In the double stage organic Rankine cycle, the working fluid combinations of R601-R1233zd, R601-R245fa and R123-R245fa show better performance when considering power, heat transfer area and payback period, which are 1760 kW, 2108.9 m2 and 4.21 year, respectively for R601-R245fa. In the added double stage organic Rankine cycle, cycle 1 and cycle 2 are connected in two stages and cycle 1 and cycle 3 in parallel. The net power of the working fluid combinations of R123-R245fa and R123-R1233zd are 1799 kW and 1782 kW, respectively, which are higher than those of the others. Further, for R123-R245fa, the heat transfer area and payback period are 3352 m2 and 6.20 year, respectively, which is better compared to those of other working fluid combinations.

Abstract The present work attempts to devise an efficient method utilizing an on-grid photovoltaic-thermal heat pump water heater (PV-THPWH) integrated with a real-time variable frequency controller to achieve the goal of energy-efficient buildings. The prime focus is to reduce the grid's dependence on the compressor's energy-intensive operation by employing a feedback-controlled variable frequency drive (VFD). Additionally, the possibilities involved with addressing the electrical and thermal energy requirements of an energy-efficient building was investigated utilizing the proposed system. R-32 refrigerant in the photovoltaic-thermal (PV-T) evaporator coils of the heat pump assembly help to cool the photovoltaic (PV) panel while delivering the absorbed heat in the condenser to heat water contained inside the storage tank. Outdoor experiments and theoretical investigations of the combined system were carried out to appraise the dynamic behavior under varying solar irradiation and ambient temperature conditions. The observations conveyed that the PV-THPWH system succeeded in reducing the PV panel operating temperature by 25%, which resulted in a 20% increment in PV power output. Also, the performance indicators, such as the instantaneous energy efficiency and instantaneous PV efficiency, were found to increase by 15% and 34%, respectively, resulting in an average coefficient of performance of 6.4. For a clear sky day, the recorded total PV energy output was 4.67 units, while the VFD compressor consumption was 3.42 units, and the surplus 1.25 units were sent to the grid. Furthermore, the economic analysis reported a payback period of 2.3 years for the developed PV-THPWH system.

Abstract Date biomass is a carbon-rich renewable resource that can be considered a potential carbon-rich substrate for energy generation over anaerobic digestion (AD). However, due to its complex nature, appropriate pretreatment is necessary to achieve a higher methane yield. Hence, the current study was envisioned to evaluate the influence of three different pretreatment strategies, namely acid, alkali, and hydrothermal pretreatment on biochemical methane potential (BMP) of seven diverse sorts of Algerian date biomass, namely Pedicels, Fibrilium, Petiole, Fruit bunch, Spath, Palm, and its mixture. Among all the pretreatment conditions, alkaline pretreatment highly influenced the lignin composition of date biomass and showed higher BMP. Among all sorts of biomass, higher BMP was detected through Palm as 295.9 mL CH4/g-TS, whereas the lowest BMP values were recorded with Petiole as 226.74 mL CH4/g-TS. Among all the experimental variations, ammonium pretreated Palm biomass documented the highest substrate conversion efficiency (63.80%), which correlates well with the observed higher BMP values. Nevertheless, there was a very marginal improvement in BMP detected in the case of other pretreatment strategies compared to alkaline pretreatment. This might be due to the efficacy of the applied pretreatment method on delignification of date biomass.

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.

Coal power plants have been a major source of undesirable emissions. Despite the technological advancements in renewable energies, coal units are still in-service in many developed and developing countries due to their reliability, adequacy, and flexibility for power delivery. There are some promising technologies for cleaner operation during power production from coal, including supercritical boiler (SC) design and carbon capture and storage (CCS), however, the challenging in innovating effective methods is still open to expand the boundary of knowledge in this speciality. This paper introduces a novel and simple method for reducing CO2 emissions and improving the dynamic responses of a 600 MW SC coal power plant by Artificial Neural Network (ANN) technique. A wide-range data-driven feedforward ANN model has been identified and verified for the various operations recorded as closed-loop data-sets, which covers all situations of startup, once-through mode, and even emergency shutdown of the unit. The closed-loop SC plant model has been augmented with an inverse multivariable coordinate NN controller, developed by analogous learning algorithm to improve the plant automation. With precisely selected setpoints, as operational rules, of temperature, pressure, and earliest possible power demand signals, the automated SC plant has been capable to operate with lower coal consumption - and thus lower emissions – than the existing operation strategy during startup, normal operation, and emergency shutdown modes. The improvement in dynamic responses have been quantified through simulations with comparison with existing performance, which have resulted in an overall average reduction of 2.143 Kg/s in coal consumption.

The Nigerian government is committed to sustaining rice production to meet national demand. Nevertheless, political tension and climate-induced stressors remain crucial constraints in achieving policy targets. This study examines whether climate change and political instability significantly threaten rice production in Nigeria. First, we employed nonparametric methods to estimate the country's rainfall and temperature trends between 1980Q1 and 2015Q4. Second, we employed the autoregressive distributed lag (ARDL) technique to examine the effects of climate change and political instability on rice production. The results show that while temperature has an increasing pattern, rainfall exhibits no significant trend. The findings from the ARDL estimate reveal that rice production responds negatively to temperature changes but is less sensitive to changes in rainfall. In addition, political instability adversely affects rice production in Nigeria. We argue that Nigeria's slow growth in rice production can be traced back to the impact of climate change and political tension in rice farming areas. As a result, reducing the overall degree of conflict to ensure political stability is critical to boosting the country's self-sufficiency in rice production. We also recommend that farmers be supported and trained to adopt improved rice varieties less prone to extreme climate events while supporting them with irrigation facilities to facilitate rice production.

Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.