In this research work, the experimental investigation on effect of preheating on the performance and emissions of a direct injection diesel engine has been reported. Four different fuels were tested using the same diesel engine: crude palm kernel oil (CPKO), preheated crude palm kernel oil (PCPKO), palm kernel oil methyl ester (PKOME) and petroleum diesel (petrodiesel) as reference fuel. A novel technique of using engine coolant to preheat the CPKO to PCPKO was employed. A bypass was created from the engine for the coolant to pass through and heat a series of coiled copper tubes inside the fuel tank and on which was the PCPKO. Preheating CPKO to obtain PCPKO reduced the viscosity by 59% and improved brake torque and brake power by 14%. BTE and BSFC of PCPKO was better than CPKO by 16% and 36%, respectively because the lowered viscosity of PCPKO facilitated a better atomization during combustion. While PKOME, biodiesel, recorded 21% less carbon monoxide and 71% less THC emissions than CPKO.

Recent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human welders to select desirable end factors to achieve good weld quality in the welding process. To demonstrate its feasibility, the proposed model has been tested with data from 27 experiments using current, arc length and welding speed as control parameters to predict weld bead width. A fuzzy deep neural network, which is a combination of fuzzy logic and deep neural network approaches, is applied in the algorithm. Simulations were carried out on an experimental test dataset with the AI TIG welding algorithm. The results showed 92.59% predictive accuracy (25 out of 27 correct answers) as compared to the results from the experiment. The performance of the algorithm at this nascent stage demonstrates the feasibility of the proposed method. This performance shows that in future work, if its predictive accuracy is improved with human input and more data, it could achieve the level of accuracy that could support the human welder in the field to enhance efficiency in the welding process. The findings are useful for industries that are in the welding trade and serve as an educational tool.

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The effect of strength and toughness on the weldability of high-strength steels is very vital consideration in the offshore oil and gas industries. Improved impact toughness of high-strength steels in offshore structures enables viable exploitation of hydrocarbons in technologically challenging conditions. This article reviews improvements in the weldability and impact toughness of high-strength steels. Steels with high strength are associated with high carbon content and addition of alloying elements as they induce hardness which leads to a higher risk of brittle fracture and hydrogen-induced cracking needs. The combination of high strength with high toughness was studied by examining the toughening mechanism of thermomechanical-controlled processing steels, which have higher strength than conventional steel plates but meet the conflicting requirements of strength, toughness and weldability. The thermomechanical-controlled processing production process entails controlled rolling process combined with accelerated cooling or direct quenching to ensure stable mechanical properties of thermomechanical-controlled processing products in welded constructions. It is concluded that due to their very fine grain size and refined heat-affected zone structure, thermomechanical-controlled processing steels can be an effective cost-saving means for fabrication of offshore structures, particularly in shipbuilding, offshore platforms and pipelines for high-operating pressures.

This paper contributes to the state of the art of applications of artificial intelligence (AI) in energy systems with a focus on the phenomenon of social acceptance of energy projects. The aim of the paper is to present a novel AI-powered communication and engagement framework for energy projects. The method can assist project managers of energy projects to develop AI-powered virtual communication and engagement agents for engaging their citizens and their network of stakeholders who influence their energy projects. Unlike the standard consultation techniques and large-scale deliberative engagement approaches that require face-to-face engagement, the virtual engagement platform provides citizens with a forum to continually influence project outcomes at the comfort of their homes or anywhere via mobile devices. In the communication and engagement process, the project managers’ cognitive capability can be augmented with the probabilistic capability of the algorithm to gain insights into the stakeholders’ positive and negative feelings on the project, in order to devise interventions to co-develop an acceptable energy project. The proposed method was developed using the combined capability of fuzzy logic and a deep neural network incorporated with a Likert scaling strategy to reason with and engage people. In a mainstream deep neural network, one requires lots of data to build the system. The novelty of our system, however, in relation to the mainstream deep neural network approach, is that one can even use small data of a few hundreds to build the system. Further, its performance can be improved over time as it learns more about the future. We have tested the feasibility of the system using citizens’ affective responses to CO2 storage and the system demonstrated 90.476% performance.