The now over a decade-long electricity crisis in Pakistan has adversely affected the socio-economic development of the country. This situation is mainly due to a lack of sustainable energy planning and policy formulation. In this context, energy models can be of great help but only a handful of such efforts have been undertaken in Pakistan. Two key shortcomings pertaining to energy models lead to their low utilization in developing countries. First, the models do not effectively make decisions, but rather provide a set of alternatives based on modeling parameters; and secondly, the complexity of these models is often poorly understood by the decision makers. As such, in this study, the Analytical Hierarchy Process (AHP) methodology of Multi-Criteria Decision-Making (MCDM) has been used for the sustainability assessment of energy modeling results for long-term electricity planning. The four scenario alternatives developed in the energy modeling effort, Reference (REF), Renewable Energy Technologies (RET), Clean Coal Maximum (CCM) and Energy Efficiency and Conservation (EEC), have been ranked using the Expert Choice^® tool based on the AHP methodology. The AHP decision support framework of this study revealed the EEC scenario as the most favorable electricity generation scenario followed by the REF, RET and CCM scenarios. Besides that, this study proposes policy recommendations to undertake integrated energy modeling and decision analysis for sustainable energy planning in Pakistan.

In recent decades, there has been a constant demand for faster, longer, and safer railway networks. This also brings challenges for condition monitoring systems in modern railway vehicles. More specifically, critical parts of railway vehicles like wheels degrade over time due to various operational and environmental reasons. Different dynamic effects such as skidding/sliding over the track and the presence of contamination between wheel-rail cause various wheel defects. Faulty wheels ultimately lead to the derailment of railway vehicles. To avoid worst situations like railway derailments, various research has been conducted for developing efficient condition monitoring systems for railway wheels. In addition, there have been some commercial condition monitoring products that can be deployed with railway vehicles. These systems incorporate various sensors such as strain gauges and vision sensors to collect data for diagnosis and prognosis. Various methods have been explored but yet there is a broad research gap in terms of developing advanced onboard condition monitoring systems. With the progress in technology, advanced systems with Machine Learning/Deep Learning methods can provide more efficient and robust condition monitoring of dynamic railway systems. Considering the need for advancement in condition monitoring systems for railway vehicles, a comprehensive review of existing condition monitoring systems for railway wheels is conducted in this paper. The review is aimed at understanding the feasibility and potential of new methods for modern railways. This paper provides a detailed overview of studies on the existing wayside systems and reports their advantages and disadvantages concerning its recently emerging counterpart on-board monitoring systems. Data acquisition systems and analysis methods are critically reviewed which could assist in developing more efficient and reliable condition monitoring systems for railway wheels. This article also reviews the current progress of wayside systems and their limitations. The article is targeted at the researchers and engineers working in this domain, who can pave the way for developing advanced and cost-effective condition monitoring systems for railway wheels using modern technologies.

Electricity demand in Pakistan has consistently increased in the past two decades. However, this demand is so far partially met due to insufficient supply, inefficient power plants, high transmission and distribution system losses, lack of effective planning efforts and due coordination. The existing electricity generation also largely depends on the imported fossil fuels, which is a huge burden on the national economy alongside causing colossal loss to the environment. It is also evident from existing government plans that electricity generation from low-cost coal fuels in the near future will further increase the emissions. As such, in this study, following the government’s electricity demand forecast, four supply side scenarios for the study period (2013–2035) have been developed using Long-range Energy Alternatives Planning System (LEAP) software tool. These scenarios are Reference scenario (REF) based on the government’s power expansion plans, and three alternative scenarios, which include, More Renewable (MRR), More Hydro (MRH), and More Hydro Nuclear (MRHN). Furthermore, the associated gaseous emissions (CO₂, SO₂, NO_X, CH₄, N₂O) are projected under each of these scenarios. The results of this study reveal that the alternative scenarios are more environmentally friendly than the REF scenario where penetration of planned coal-based power generation plants would be the major sources of emissions. It is, therefore, recommended that the government, apart from implementing the existing plans, should consider harnessing the renewable energy sources as indispensable energy sources in the future energy mix for electricity generation to reduce the fossil-fuel import bill and to contain the emissions.

Despite being a limited and scarce resource, the necessity and exploitation of fossil fuels are unstoppable in serving human demands. In order to supply energy demand without causing environmental damage, it is crucial to utilize a variety of renewable feedstock resources. Biochar, made up mostly of carbon, oxygen, and hydrogen, is the product of the thermochemical processes of pyrolysis, hydrothermal carbonization, torrefaction, and hydrothermal liquefaction. Biochar, once activated, has the potential to act as a catalyst in a variety of energy generation processes, including transesterification and fermentation. Transesterification is the process that is used to produce biodiesel from a variety of oils, both edible and non-edible, as well as animal fats in the presence of either a homogeneous or a heterogeneous catalyst. When selecting a catalyst, the amount of free fatty acid (FFA) content in the oil is considered. Homogeneous catalysts are superior to heterogeneous catalysts because they are unaffected by the concentration of free fatty acids in the oil. Homogeneous catalysts are extremely hazardous, as they are poisonous, combustible, and corrosive. In addition, the production of soaps as a byproduct and a large volume of wastewater from the use of homogeneous catalysts necessitates additional pretreatment procedures and costs for adequate disposal. This article examines the biochar-based fuel-generation catalyst in detail. At first, a wide variety of thermochemical methods were provided for manufacturing biochar and its production. Biochar’s chemical nature was analyzed, and the case for using it as a catalyst in the production of biofuels was also scrutinized. An explanation of how the biochar catalyst can improve fuel synthesis is provided for readers. Biodiesel’s transesterification and esterification processes, biomass hydrolysis, and biohydrogen generation with the help of a biochar catalyst are all reviewed in detail.

In Pakistan, around 58% of current electricity is generated from fossil fuels and only 2.4% is generated using renewable energy (RE) resources even though country is blessed with enormous RE potential. Among other RE resources, Pakistan's geographical location offers high solar energy potential, which implies that actual potential assessment should be undertaken. This study, as such, undertakes a comprehensive assessment of solar energy potential and prospects of solar photovoltaic (PV) systems for both off‐grid and grid‐connected systems. This study also estimates the future available capacity of rooftop and rural off‐grid solar PV capacity. Three different types of solar PV modules of the same size, that is, thin‐film, premium, and standard were modeled to compare energy outputs. NREL's System Advisor Model (SAM) is used to estimate the geographical and technical potential of solar PV considering updated data and geographical information. SAM results suggest that an average of 4.5 kWh/kWp/day is obtained from an installed capacity of 1 KWp. The logistic modeling equations are further used to forecast the solar PV penetration over a period until 2090. The research investigation concludes that 2.8 × 10⁶ GWh of electricity can be generated annually in Pakistan. The estimated results prove that solar PV has the potential to meet the present as well as future energy needs of Pakistan.