This paper proposes a concept of knowledge engineering based innovative approach for seeking solutions related to electric al engineering systems. The knowledge base approach is discussed for its effectiveness at preliminary stages of solution hunting and solution design, which may reduce the iterations of design process and save time/cost. While referring research literature, this paper builds a hypothesis for novel and efficient usage of knowledge engineering tools for Electrical Engineers. The research seeks development of a methodological tool, which will be generic for aimed sub-sector (e.g. power distribution) of electrical systems. Based on structured innovation approach, this tool will provide conceptual guidance and direction to find solutions in sector specific electrical system problems. This structured approach and electrical engineering focus of the tool will facilitate electrical engineers for reaching practical and effective solutions with less expertise and time.
PL
W artykule zaproponowano zastosowanie metod inżynierii wiedzy do rozwiązywania problemów związanych z systemami elektrycznymi. Pozwala to na ograniczenie liczby iteracji przy projektowaniu i skraca czas projektu.
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Purpose: This paper explores the capabilities of genetic algorithms in handling optimization of the critical issues mentioned above for the purpose of manufacturing process planning in reconfigurable manufacturing activities. Two modified genetic algorithms are devised and employed to provide the best approximate process planning solution. Modifications included adapting genetic operators to the problem specific knowledge and implementing application specific heuristics to enhance the search efficiency. Design/methodology/approach: The genetic algorithm methodology implements a genetic algorithm that is augmented by application specific heuristics in order to guide the search for an optimal solution. The case study is based on the manufacturing system. Raw materials enter the system through an input stage and exit the system through an output stage. The system is composed of sixteen (16) processing modules that are arranged in four processing stages. Findings: The results indicate that the two genetic algorithms are able to converge to optimal solutions in reasonable time. A computational study shows that improved solutions can be obtained by implementing a genetic algorithm with an extended diversity control mechanism. Research limitations/implications: This paper has examined the issues of MPP optimization in a reconfigurable manufacturing framework with the help of a reconfigurable multiparts manufacturing flow line. Originality/value: The results of the case illustration have demonstrated the practical use of diversity control implemented in the MGATO technique. In comparison to MGAWTO, the implemented MGATO improves the population diversity through a customized threshold operator. It was clear that the MGATO can obtain better solution quality by foiling the tendency towards premature convergence.
Carboxymethyl cellulose (CMC) was used in the chemical reduction using sodium borohydride to yield dispersive nano zero-valent iron (nZVI) particles as reactive and stable adsorbents. CMC- -stabilized nZVI particles were characterized via UV-visible light spectroscopy, X-ray diffraction, dynamic light scattering, transmission electron microscopy, and specific surface area assisted using a probe ultrasonication dispersing tool at 50% amplitude power. High catalytic reactivity obtained in pseudo-first order reaction for Cr6+ (rate constant K1 = 0.0311 min–1) and pseudo-second order for Cu2+ (rate constant K2 = 0.0946 g·mg–1·min–1) indicated that colloidal stability of nZVI particles can be achieved with a stabilizer for the removal of toxic contaminants.
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