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The aim of this paper is to study experimentally and numerically heat transfer enhancement and friction factor inside a counter flow concentric tube heat exchanger using different volume fractions of CuO, Al2O3, and TiO2 nanofluids. Using nanofluids increases the thermal conductivity and conductive heat transfer coefficients compared to the base fluids. Increased heat transfer coefficients reduce the required tube length leading to reduced pressure drop in heat exchangers. Nanofluids have a high specific surface area, and therefore more heat transfer surface between particles and fluids is available. A standard k-ε turbulence model based on two equations was used in this investigation to solve the momentum and energy equations of nanofluids. The results were presented for Reynolds number range between 2000 and 12,000 and two different volume fractions of 0.05 and 0.2%, respectively. The numerical results illustrated that all the tested nanofluids exhibited better heat transfer coefficients compared with pure water. Particularly, CuO nanofluid illustrated the highest performance index for both concentrations. The performance index varied from 4.25, 3, and 2.5 to 1.5 for CuO, Al2O3, and TiO2 nanofluids, respectively, for the whole range of Reynold numbers. The results of this investigation may be used in designing heat exchangers-based nanofluids that can be used in many energy applications such as solar, energy production systems, and any industrial process.

The major goal of this study was to develop a robust fuzzy model to mimic the generation of biodiesel from the transesterification of dairy-washed milk scum (DWMS) oil. Four process parameters were considered: the molar ratio of methanol to oil, the concentration of KOH, the reaction temperature, and the reaction time. The proposed technique was divided into two steps: fuzzy modelling and optimum parameter identification. The capability of fuzzy tools to capture and make use of linguistic variables and fuzzy sets is one of their main benefits. This means that fuzzy logic allows for the representation and manipulation of values that fall across a continuum rather than merely relying on crisp values or binary categories. When dealing with non-linear relationships, this is especially helpful since it gives a more accurate and nuanced depiction of the underlying data. As a result, an accurate fuzzy model was initially built based on collected data to simulate the biodiesel production in terms of the molar ratio of methanol to oil, the concentration of KOH, the temperature of the reaction, and the reaction duration. In the second phase, the beetle antennae search (BAS) algorithm was applied to identify the optimal values of the process parameters to boost the production of biodiesel. The BAS algorithm draws inspiration from beetle behavior, particularly how they navigate using their antennae. It employs a swarm-intelligence method by deploying virtual beetles that swarm over the problem area in search of the best solution. One of its main features is the BAS algorithm’s capacity to balance exploration and exploitation. This is accomplished through the algorithm’s adaptable step-size mechanism during the search phase. As a result, the algorithm can first investigate a large portion of the problem space before gradually moving closer to the ideal answer. Compared with ANOVA, and thanks to fuzzy, the RMSE decreased from 7 using ANOVA to 0.73 using fuzzy (a decrease of 89%). The predicted R2 increased from 0.8934 using ANOVA to 0.9614 using fuzzy (an increase of 7.6). Also, the optimisation results confirmed the superiority of the BAS algorithm. Biodiesel production increased from 92% to 98.16%.

Overheating of photovoltaic (PV) panels decreases their efficiency and lifetime, and subsequently increases the levelized cost of energy (LCOE). Passive PV cooling would enhance the PV operational stability and durability. The cooling tower (CT) technology offers an attractive approach for zero-cost capability. In this work, we developed and customized a CT specific for passive PV cooling. Since the dense downdrafted cooled air gained high velocity, a turbine was installed at the bottom of the CT for power production. At the height’s ambient temperature, the CT cooled the air from 50 °C down to 30 °C. The cooled air at 30 °C has enough capacity to cool the PV panels. This cooling capacity improved the average annual efficiency of the PV panels by 6.83%. The design specifications of the CT have the highest performance, with the maximum radius of PV area of panels that can be cooled at 50 m. Furthermore, the current design could operate during the night for power production with minimum operational cost.

The use of aqueous carbonate as an inorganic absorbent is not only inexpensive but also stable and environmentally friendly. However, the regeneration processes for aqueous carbonate sorbents require high regeneration heat duty; this energy intensity makes their wide utilization unaffordable. In this work, a low-temperature, energy-saving, and environmentally friendly carbon dioxide desorption method has been investigated in potassium bicarbonate-water-alcohol solutions. The addition of alcohol, particularly isopropanol, to the potassium bicarbonate-water solution can significantly increase carbon dioxide desorption capacity. The potassium bicarbonate-water-isopropanol solution used in this study (36 wt % isopropanol) resulted in 15.2 mmol of carbon dioxide desorption within 2400 s at 80 °C, which was 2000-fold higher than the potassium bicarbonate-water-solution. This research demonstrates a water-lean solvent-based carbon dioxide removal route with the potential to be economical, environmentally safe, and energy-efficient. CO2 sequestration, capture, and utilization technologies will play a key role in reducing CO2 emissions. The excellent desorption kinetics and relatively moderate desorption temperatures (80 °C) of water-lean solvent could help in reducing the cost of CO2 capture, particularly in terms of the heat demand at the regenerator.

Green hydrogen is considered to be one of the best candidates for fossil fuels in the near future. Bio-hydrogen production from the dark fermentation of organic materials, including organic wastes, is one of the most cost-effective and promising methods for hydrogen production. One of the main challenges posed by this method is the low production rate. Therefore, optimizing the operating parameters, such as the initial pH value, operating temperature, N/C ratio, and organic concentration (xylose), plays a significant role in determining the hydrogen production rate. The experimental optimization of such parameters is complex, expensive, and lengthy. The present research used an experimental data asset, adaptive network fuzzy inference system (ANFIS) modeling, and particle swarm optimization to model and optimize hydrogen production. The coupling between ANFIS and PSO demonstrated a robust effect, which was evident through the improvement in the hydrogen production based on the four input parameters. The results were compared with the experimental and RSM optimization models. The proposed method demonstrated an increase in the biohydrogen production of 100 mL/L compared to the experimental results and a 200 mL/L increase compared to the results obtained using ANOVA.