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A biomass product and process network that displays how organic waste and other non-traditional biomass feedstocks may be converted into useful bioproducts and biofuels is a necessary addition to the field of biomass conversion and utilization. We develop a processing network of 216 technologies and 172 materials/compounds that contains conversion pathways of agricultural and organic waste biomass sources, such as food peels, animal manure, and grease. To examine the effectiveness and economic feasibility of these conversion pathways, the biomass product and process network is optimized for return on investment. The resulting problem is a data-driven two-stage adaptive robust mixed-integer nonlinear fractional program, which was effectively solved via a tailored optimization algorithm. The proposed approach is applied to two case studies in which traditional agricultural feedstocks are used alongside biological and agricultural waste feedstocks. The selected feedstocks were used to satisfy and, in some cases, even exceed demand for selected products. The optimal pathways have returns on investment of 26.1% and 6.2%, with utilized conversion technologies ranging from hydrocracking to microwave hydrodiffusion. In both cases, we find that profitable processing pathways are utilized at maximum capacities to increase return on investment. Specifically, in the case study where orange peel wastes are used to produce pectin, we find that this pathway is highly profitable at the given market price. The two cases that are run using the proposed model are then compared to additional cases to display differences that arise when uncertainty is not considered and the objective function of the model is changed. • The expanded biomass product and process network is described. • Two-stage data-driven ARO is applied to a biomass product and process network. • Case studies, computational results, and future applications are discussed. • A novel application of the proposed computational algorithm is presented. [ABSTRACT FROM AUTHOR]

Manufacturers lack the economic tools to properly perform maintenance procedures to increase manufacturing reliability while reducing system energy consumption. A return on investment strategy is developed to provide plant managers with a quantitative method to optimize the maintenance actions that lead to the largest return on investment. Over the long term, this energy economic analysis results in the largest decrease in energy costs for a manufacturing facility. A control methodology is developed to increase profits on a daily basis by inserting energy opportunity windows at various machines thus reducing energy consumption with minimal production impact. A simulation case study is performed to validate the return on investment strategy and to test the control methodology's impact on the overall profit of the facility. [ABSTRACT FROM AUTHOR]

Smart homes have the potential to achieve efficient energy consumption: households can profit from appropriately scheduled consumption. By 2020, 35% of all households in North America and 20% in Europe are expected to become smart homes. Developing a smart home requires considerable investment, and the householders expect a positive return. In this context, this work addresses the following question: what and/or when equipment should be bought for a specific site to gain a positive return on the investment? This work proposes a framework to guide the smart-home transition considering customized electricity usage. The framework is based on linear models and gives a simple payback analysis of each combination of equipment acquisition for any specific user taking into account geographical location and local conditions. It also possible to use the framework for equipment sizing. The results quantify the dependence of the simple payback on the site and the application. • Modeling and optimization of the integration of smart home components. • General framework for the optimal set of smart appliances and investment timing. • Maximization of the return on investment. • Economic feasibility shown for three case studies in different countries using real data. • Quantification of the payback for each site and application. [ABSTRACT FROM AUTHOR]