A contribution to sustainable management of integrated material/energy networks in process industries

Towards sustainability forces process industries to change their traditional patterns. Therefore, efficient retrofitting has been a significant challenge and raised several issues, motivating the process system engineering (PSE) to develop models. These models mainly aim to optimize profitability, cost reduction, energy consumption, demand satisfaction, the environmental impact associated with the production process, and social acceptance. Nevertheless, such optimization is significantly complicated if considering the presence of uncertainty and seeking compromised outcomes. This thesis aims to extend a general model to facilitate retrofitting in industrial processes and expedite optimization of the issues. Such a contribution based on developing efficient mathematical models allows coordinating many decision variables synchronizing the production and distribution tasks in terms of economic and environmental criteria. This thesis presents an overview of the retrofitting requirement towards sustainable material/energy networks, describing and analyzing the current methods, tools, and models used and identifying the most relevant open issues. The second part focused on developing current models stressing energy integration in the processing system. This part first explores how the economic performance of the network can be enhanced and environmental impacts improved simultaneously by integrating an energy generation unit into the production system. Furthermore, the network sustainability performance was explored under demand uncertainties. Additional risk indicators (including financial and environmental risk metrics) have been included to add risk management capability to the model. This part also explores the strategies that efficiently select the number of scenarios. Consequently, a novel generalized mathematical formulation that integrates equations regarding energy generation and material production decision variables is efficiently solved. The effect of uncertainty on the economic and environmental performance is analyzed by using risk analysis. Finally, the model was extended to solve multi-renewable energy generation integrated into the multi-product production process under demand uncertainty. The importance and effect of the energy/material integration over the network configuration are analyzed through sensitivity analysis. The third part of this thesis provides the conclusions and further work to be developed.El camino hacia la sostenibilidad obliga a las industrias de procesos a cambiar los patrones de trabajo tradicionales. La modernización eficiente es un importante desafío que plantea múltiples problemas, lo que ha llevado a la ingeniería de sistemas de procesos (PSE) a desarrollar modelos que intentan no solo optimizar la rentabilidad, reducir de costes o satisfacer demanda de productos y servicios, sino también afrontar el impacto ambiental asociado al proceso productivo y la aceptación social de dicho proceso, en entornos volátiles, en los que la consideración de la incertidumbre asociada al escenario de trabajo es esencial. Esta tesis tiene como objetivo ampliar y flexibilizar los modelos de gestión de cadena de suministro existentes para facilitar la retroadaptación de los procesos industriales y agilizar la optimización de los problemas de toma de decisiones en este entorno. Tal aporte está basado en el desarrollo de modelos matemáticos eficientes que permite coordinar diferentes variables de decisión, sincronizando las tareas de producción y distribución en términos de criterios económicos y ambientales. Para ello, en primer lugar se presenta una visión general del requisito de adaptación hacia redes de materiales / energía sostenibles, describiendo y analizando los métodos, herramientas y modelos actuales utilizados e identificando los problemas abiertos más relevantes. La segunda parte de esta Tesis se centra en el desarrollo de modelos que enfatizan la integración energética en el sistema de proceso. Esta parte explora primero cómo se pueden mejorar simultáneamente el desempeño económico de la red y los impactos ambientales mediante la integración de unidades de generación de energía en el sistema de producción. Además, se ha explorado el rendimiento de la sostenibilidad de la red bajo incertidumbre en la demanda. Se han incluido indicadores de riesgo adicionales (incluidas métricas de riesgo financiero y ambiental) para incorporar en el modelo la capacidad de gestión de riesgos. Esta parte también explora las estrategias que seleccionan de manera eficiente el número de escenarios a considerar. En consecuencia, se resuelve de forma eficiente una formulación matemática novedosa que generaliza e integra ecuaciones relativas a decisiones de producción energética y de materiales. El efecto de la incertidumbre sobre el desempeño económico y ambiental se analiza mediante análisis de riesgo. Finalmente, el modelo se amplía para abordar la utilización coordinada de diferentes formas de energía renovable, y su integración en el proceso de producción multi-producto bajo incertidumbre en la demanda. La importancia y el efecto de la integración de la toma de decisiones sobre la configuración de la red se discuten a través de diferentes análisis de sensibilidad. La tercera parte de esta tesis resume las conclusiones de todos estos trabajos y plantea ampliaciones y nuevas líneas de mejora que surgen a partir de los modelos y procedimientos desarrollados en esta tesis.Postprint (published version

Optimal Design and planning supply chains of multi renewable resource-based energy/material applied in process industries

In this work, the effects of multi renewable resource-based energy integration in material supply chains are analysed under stochastic and deterministic conditions. The proposed approach is based on the development of a Multi-objective model to optimize the design and planning decisions of country-size SCs in the existence of conflicting objectives. A multi-scenario mixed-integer linear model is proposed and the capabilities of the approach are examined through a case study from a Sugar cane industry. The integrated energy/material supply chains model shows more economical feasibility than standalone ones and the stochastic solutions in this work demonstrate more robustness in the economic performance of the SC comparing to the deterministic one at any level of the environmental impact. In fact, the stochastic approach optimizes the conflicting objectives as well as improves the whole system robustness compared to the deterministic one and should be therefore the preferred choice in practice.Postprint (published version

Systematic approach for the design of sustainable supply chains under quality uncertainty

Sustainable process ing ha s attracted recently an increasing interest in the process systems engineering literature. From a practical perspective , addressing sustainability inevitably require s a Multi - Objective analysis and optimization to evaluate the environmental impact in additi on to the economic performance. Bio - based process es are typically used in order to promote sustainability, although the quality of the bio - mass us ually is variable and present an uncertainty behavior . However, the approaches presented so far to address mul ti objective problems under uncertainty have focused on simplifying the optimiz ation model reducing the set of feasible choices and neglecting the potential effects of quality variations streams on the overall system performance . We present here an altern ative approach based on the State Task Network formulation capable to address M ulti - O bjective Optimization problems under uncertain conditions, which includes the material quality effect, all together. The resulting set of solutions are then assessed using ELECTRE IV method, which identif ies the ones showing better overall performance within the uncertain parameters spac

Systematic approach for the design of sustainable supply chains under quality uncertainty

Sustainable process ing ha s attracted recently an increasing interest in the process systems engineering literature. From a practical perspective , addressing sustainability inevitably require s a Multi - Objective analysis and optimization to evaluate the environmental impact in additi on to the economic performance. Bio - based process es are typically used in order to promote sustainability, although the quality of the bio - mass us ually is variable and present an uncertainty behavior . However, the approaches presented so far to address mul ti objective problems under uncertainty have focused on simplifying the optimiz ation model reducing the set of feasible choices and neglecting the potential effects of quality variations streams on the overall system performance . We present here an altern ative approach based on the State Task Network formulation capable to address M ulti - O bjective Optimization problems under uncertain conditions, which includes the material quality effect, all together. The resulting set of solutions are then assessed using ELECTRE IV method, which identif ies the ones showing better overall performance within the uncertain parameters spac