Water desalination by membrane distillation (MD) can be affected by a wide range of operating parameters. The present work uses combinational approach of Analytical Hierarch process (AHP) and Fuzzy Analytical Hierarchy process (Fuzzy-AHP) to identify the most important parameters in the MD desalination. Five process parameters and key-performance indicators, named derivable outputs (DOs), are considered, along with the critical factors affecting these DOs in the current study. The DOs and the critical influencing factors (CIFs) are selected based on their experimental feasibility. The investigation involves five DOs, which are liquid entry pressure, thermal power consumption, permeate quality, permeate flux, and pumping (feed circulation) power. A total of twenty-five critical influencing factor were associated with the DOs. The identification of the DOs and CIFs was based on the literature review, and further analyses were performed. Both methods, AHP and Fuzzy-AHP, determined six extremely important CIFs in the desalination MD, which are feed temperature, feed concentration, or feed salinity; feed flow rate; membrane hydrophobicity; pore size; and membrane material. Moderately important CIFs are found to be four by both methods. These common CIFs are feed solution properties, membrane thickness, feed channel geometry, and pressure difference along the feed channel. Finally, the least preferred CIFs are four common in both methods that are MD configuration, duration of test, specific heat of feed solution, and viscosity.

Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.

Alternative fuels are found to be the most promising solution to the problem of conventional IC engine pollution because their use curtails its huge emissions without exerting a negative impact on its performance. In this research, a conventional compression ignition engine is investigated by operating it with the combination of simulated biogas and neat diesel under a dual fuel mode of operations. The simulated biogas in the current work comprises different proportions of methane and carbon dioxide in the mixture. The full factorial approach in this work involved studying the effects of parameters such as biogas flow rate, composition, intake temperature, torque, and methane enrichment (complete removal of CO2 from biogas) on the engine performance, emissions, and combustion indices with an extensive number of experiments. It is witnessed from the research that biogas is capable of providing a maximum of 90% of the overall energy input, while the CI engine operates under dual fuel mode. Under the dual fuel mode of operation involving biogas, a significant amount of reductions are witnessed in secondary fuel consumption (67%), smoke (75%), and NOx (55%) emissions. At low flow rates, biogas is found to improve brake thermal efficiency (BTE), whereas it reduces hydrocarbon and carbon monoxide emissions. Methane enrichment resulted in more diesel substitution by 5.5% and diminishes CO and HC emissions by 5% and 16%, respectively. Increasing the intake temperature caused an increase in thermal efficiency (2%) and a reduction in diesel consumption (~35%), and it curtailed all emission elements except NOx.