Due to the increased demand for global transportation needs, a long-range diesel engine is considered an important prime mover to fulfill the transportation demand. The major problem addressed by the diesel engines is it liberates harmful emissions and it also increases global warming. CO2 is considered an important greenhouse gas and it has to be controlled by diesel engines. In this research, extensive experimental work is done to identify a suitable solution to control CO2 emissions. For five different mole concentrations (0.1 to 0.5), three types of amino solutions (L-alanine, L-aspartic acid, and L-arginine) were prepared. By passing diesel exhaust through each solution, CO2 absorption is investigated. For CO2 absorption, an appropriate CO2 control system is built and tested. The tests were carried out in a diesel engine which is a naturally aspirated single-cylinder engine with a water-cooled system. It was possible to obtain an effective CO2 reduction of up to 90%, as well as a simultaneous reduction in HC and CO.

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Diesel-powered transportation is considered an efficient method of transportation; this sees the increase in the demand for the diesel engine. But diesel engines are considered to be one of the largest contributors to environmental pollution. The automobile sector accounts for the second-largest source for increasing CO2 emission globally. In this experiment, a suitable postcombustion treatment to control CO2 emission from IC engine exhaust is developed and tested. This work focuses to control CO2 emission by using the chemical adsorbent technique in diesel engine exhaust. An amine-based liquid is used to adsorb the CO2 molecules first and absorb over the amines from the diesel engine exhaust. Three types of amino solutions (L-alanine, L-aspartic acid, and L-arginine) were prepared for 0.3 mole concentrations, and the CO2 absorption investigation is performed in each solution by passing the diesel exhaust. A suitable CO2 adsorption trap is developed and tested for CO2 absorption. The experiments were performed in a single-cylinder diesel engine under variable load conditions. The eddy current dynamometer is used to apply appropriate loads on the engine based on the settings. The AVL DIGAS analyzer was used to measure the CO2, HC, and CO emissions. An uncertainty analysis is carried out on the experimental results to minimize the errors in the results. The effective CO2 reduction was achieved up to 85%, and simultaneous reduction of HC and CO was also observed.

The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.