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Reversible solid oxide cells can provide efficient and cost-effective scheme for electrical-energy storage applications. However, this technology faces many challenges from material development to system-level operational parameters , which should be tackle for practical purposes. Accordingly, this study focuses on developing novel robust artificial intelligence-based blackbox models to optimize operational variables of the system. A genetic-programming algorithm is used for Pareto modeling of reversible solid oxide cells in a multi-objective fashion based on experimental input-output data. The robustness of the obtained optimal model evaluated using Monte Carlo simulations technique. An optimization study adopted to optimize the operating parameters, such as temperature and fuel composition using a differential evolution algorithm. The objective functions that have been considered for Pareto multi-objective modeling process are training error and model complexity. In addition, the discrepancy between maximum and minimum output voltage in the whole operation of the system is chosen as the optimization process objective function. The robustness of the optimal trade-off model is shown in terms of statistical indices for varied uncertainty levels from 1 to 10%. The optimized operational condition based on the suggested model reveals optimal intermediate temperature of 762 °C and fuel mixture of about 29% H 2 , 25% H 2 O, and 14% CO.

Purpose This paper is based on a study of six similar buildings built in Gothenburg, Sweden, in 1971, which were in urgent need of renovation. A life cycle profit analysis shows how four competing concepts were evaluated to find a financially viable renovation concept; additionally, the environmental impacts of these renovation concepts using a life cycle assessment are presented. Design/methodology/approach Four renovation concepts are compared to find the most appropriate concept, namely, minimalist, code-compliant, low-energy and low-energy plus vertical extension concepts. The methods used for comparison are life cycle profit analysis and life cycle impact assessment; the methods used for data gathering included site visits, interviews, document study, co-benefits study and energy simulation. Findings The findings show that vertical extension supported the energy-efficient renovation of the buildings and that the combination of low-energy and the vertical extension had the highest return on investment and the lowest environmental impact. The selected concept for renovating the remaining five buildings combined was the low-energy plus vertical extension. Additional benefits from vertical extension include more apartments in central locations for the housing company, a wider variety of apartment layouts and a wider range of tenants. Drawbacks include increased use of infrastructure, green space and common appliances, as well as gentrification. Originality/value This study shows how a vertical extension can financially enable an energy-efficient renovation and further lower its environmental impact. Benefits and drawbacks of densification are also highlighted to better understand the implementation of vertically extending a building.