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Hydrogen Energy cycles

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Frank Jordan
added 2 research items
Many intended to be useful inventions are not commercially successful because they do not meet market requirements. They are designed by engineers who are experts in their field, but they do not know the market requirements for their customers. The following article demonstrates how a hydrogen energy cycle device can be successfully introduced into the market with a clear and profitable business model under the current price situation. The business model is based on real market data and it can be demonstrated that an entire industry can be developing out of this business similar to the PV business. With clearly explained overview, substantiated and backed up by statistical data, a successful and profitable business plan is developed. It gives potential investors a clear picture of the technology and the market potential with the profits, benefits as well as quantity of customers.
Many inventions are not commercially successful because they do not meet market requirements. Engineers who are experts in their field design them, but they do not know the market requirements of the customers. The following article demonstrates how a hydrogen energy cycle device can be successfully designed to meet market requirements. These requirements can be obtained by analysing the bulk of customer data and known facts. Masses of data will be evaluated. Statistical methods will be used to find a design that potentially can be successful in the market. With clearly explained decision trees, substantiated and backed up by statistical data a potentially successful configuration was found. This article demonstrates that alternative energy design needs are different from the classic grid supported power solutions. It should be demonstrated how slim and flexible design can be, to avoid individual customization and permanent reengineering.
Frank Jordan
added 2 research items
AI supported Maintenance is the road map for middle east countries to liberated from foreign countries labour source dependencies.
KPIs (key performance indicators) are currently widely used in the industries at management level and in the toolkit of the consulting companies. However, they are interpreted by humans, and humans act on the results based on the experience of an individual. What is good,bad or underperforming is determined by fixed setpoints based on recognised industry benchmarks. Dynamic setpoints that are based on individual company or market circumstances are not common or even unheard of. KPIs are not automatically fed back into the control cycle of managing a company or an operational plant by a computerized business model. In general, they are high level in nature and do not go down to the nucleolus of the production process and operating plant equipment. Therefore, simplifications and reduction of data are necessary to make it manageable for decision-makers. However, in the time of cloud computing, deep learning, and AI science, it is possible to analyse the performance of infinitly small parts/equipment of a processing plant. The resulting data can be amalgamated from the bottom up to give precise results, the possibility to act instantaneously and the ability to identify the root cause of any issues. This article intends to offer potential solutions to how KPIs can be utilized for the digital transformation of any industryfor improving processes and business opportunities.