Carbon emission trading for the design of sustainable chemical supply chain networks under uncertainty

La reducción de la emisiones de CO2 es un objetivo prioritario en el diseño de cualquier planta procesos químicos. Es trabajo estudia la idoneidad de utilizar una política denominada "Carbon Trading", que ya se está aplicando en algunos países. Así, las autoridades limitan la máxima cantidad de CO2 que se puede emitir a la atmósfera (medidas a partir del indicador medioambiental Global Warming Potential, GWP) por el conjunto de las industrias de una determinada región. Además, cada industria (o planta) tiene derecho a emitir una determinada cantidad de CO2. Si una planta en particular no es capaz de emitir a un valor igual o por debajo de este límite, entonces sufre una penalización económica que le obligará a comprar lo que se denomina derechos de emisión de CO2 por la cantidad que excede de dicho límite. Si por el contrario una determinada planta decide cambiar su proceso o su forma de trabajar para emitir CO2 por debajo de ese límite, entonces puede conseguir ingresos extras vendiendo derechos de emisión de CO2 a otras plantas que lo necesiten. De esta forma, se establece un mercado de derechos de emisión donde el precio unitario de venta del derecho de emisión de CO2 no se puede predecir (se trata de un parámetro incierto). Y por ese motivo, se requiere lo que se denomina una diseño robusto de una planta, no se diseña para un valor fijo de un parámetro (en este caso un valor fijo del precio de la emisión de CO2) si no que se busca un diseño que sea capaz de comportarse de la mejor forma cuando el valor del parámetro incierto cambie

Towards an integrated framework for supply chain management in the batch chemical process industry

There is a large body of work on supply chain (SC) optimization in the chemical process industry (CPI). However, some of the basic aspects of the optimization problem are not adequately handled by the models and solution strategies developed so far in the literature. This paper focuses on the underlying philosophy of our approach to supply chain management (SCM) in the CPI, which aims to overcome the challenges posed by this problem. Two main topics that offer great opportunities for improvement in SCM are discussed. These are the development of modeling approaches and solution strategies that reflect SC dynamics, the inclusion of environmental considerations, and the incorporation of novel business aspects and key performance indicators (KPI) into the existing formulations to enlarge the scope of SC analysis, which is currently rather limited. Our integrated solution strategy for SCM, which covers the aforementioned aspects and implements the ideas and concepts developed in our research, is also presented and its advantages are highlighted in a case study

Development of advanced mathematical programming methods for sustainable engineering and system biology

The main goal of this thesis is to develop advanced mathematical programming tools to address the design and planning of sustainable engineering systems and the modeling and optimization of biological systems. First we introduce a novel framework for the coupled use of Geographical Information Systems (GIS), Mixed-Integer Linear Programming (MILP) and decomposition algorithm for GIS based MILP models. Our approaches combine optimization tools, spatial decision support tools, economic and environmental analysis. Second we propose the general framework for sustainable design of energy systems like heat exchanger networks and utility plant. Our method is based on the combined use of the multi-objective optimization tools, Life Cycle Assessment methodology (LCA) and a rigorous dimensionality reduction method that allows identifying key environmental metrics. Finally we introduce multi-objective Mixed-Integer Non-Linear Programming (MINLP) based method for identifying in a rigorous and systematic manner the most probable biological objective functions explaining the operation of metabolic networks.El objetivo principal de esta tesis es el desarrollo de herramientas de programación matemática para abordar el diseño y planificación de procesos industriales sostenibles y la optimización en el área de la biología de sistemas. Primeramente se establece un nuevo marco para el uso simultáneo de Sistemas de Información Geográfica (GIS), Programación Lineal Entera Mixta (MILP) y algoritmos de descomposición para modelo basados en MILP-GIS. Nuestros enfoques combinan herramientas de optimización, herramientas espaciales para la toma de decisiones y análisis económicos y medioambientales. En segundo lugar, se propone el marco general para el diseño de sistemas de energía sostenibles, como las redes de intercambio de calor y plantas de servicio para la industria del proceso. Nuestro método se basa en el uso combinado de herramientas de optimización multiobjetivo, metodología de Análisis de Ciclo de Vida (LCA) y un riguroso método de reducción de dimensionalidad que permite la identificación de indicadores ambientales clave. Finalmente introducimos un método basado en Programación Multiobjetivo Mixta Entera no Lineal (MINLP) aplicado a la identificación rigurosa y sistemática de las funciones objetivo biológicas más probables que explican el funcionamiento de las redes metabólica

Simulateur multiagent d'un réseau de création de valeur : application à l'industrie forestière

Les décisions de planification à l'intérieur d'un réseau de création de valeur sont multiples et peuvent entraîner de lourdes conséquences pour une entreprise. Différents outils sont disponibles pour aider les personnes en charge de prendre ces décisions en offrant un suivi sur la production, le transport, les inventaires, etc. Ce mémoire propose la conception d'un simulateur basé sur une plateforme dc planification multiagent déjà existante. Pour se faire, différents mécanismes de simulation devront être implantés, principalement la gestion du temps. De plus, un nouvel agent a été développé afin de simuler le rôle de clients dans un réseau de création de valeur de bois d'oeuvre. De plus, cet agent permet dc simuler les deux types dc relation d'affaires qui sont les plus courantes dans cette industrie. La dernière contribution est la conception d'un cas d'étude pour démontrer les capacités dc ce simulateur à travers différentes expérimentations

Optimization of environmentally friendly solar assisted absorption cooling systems

La optimización de los sistemas de conversión de energía gana cada vez más importancia debido a su impacto ambiental y los limitados recursos de combustibles fósiles. Entre estos sistemas los de refrigeración tienen una contribución creciente en el consumo total de energía y en las emisiones de CO2. Los sistemas de absorción operados con energía solar son una de las alternativas más sostenibles frente a los sistemas de refrigeración convencionales. Por lo tanto, este trabajo se centra en su mejora siguiendo los métodos de optimización termo-económica y de programación matemática. El análisis exergético y la optimización termo-económica basada en el método estructural se han realizado para distintas configuraciones de ciclos de refrigeración por absorción con las mezclas de trabajo agua-LiBr y amoniaco-agua. En la sección de programación matemática se incluye la optimización multi-objetivo (frontera de Pareto), la optimización bajo incertidumbre de los precios de la energía, el uso de varios indicadores de impacto ambiental y el efecto del impuesto sobre las emisiones de CO2. Los resultados demuestran que se pueden obtener reducciones importantes del impacto ambiental frente a los sistemas convencionales. Los sistemas de refrigeración solar no sólo son atractivos para reducir el impacto ambiental, sino también pueden ser económicamente competitivos. Su implantación dependerá, en gran medida, del impuesto sobre las emisiones de CO2 y del coste de la energía.Optimizations of energy conversion systems become more important because of their environmental impact and the limitations of the fossil fuel resources. Among these systems cooling and refrigeration machines have an increasing share in the total energy consumption and contribution to CO2 emissions. Solar assisted absorption cooling systems are sustainable alternatives compared to the conventional cooling systems. Hence, this work is focused on improving the sustainability of cooling systems following the thermoeconomic optimization and mathematical programming approaches. In the first approach the energy, exergy and structural analysis are performed for different configurations of water/LiBr and ammonia/water absorption cooling cycles. In the second approach multi-objective optimization (Pareto frontier), optimization under uncertainty of energy prices, different environmental impact indicators, and the effect of CO2 emissions tax to reduce the global warming are discussed. The results of the multi-objective optimization show that a significant environmental impact reduction can be obtained. Results indicate that these systems are attractive not only to reduce the environmental impact but also in incurring the economic benefits. However, its practical impact largely depends on the CO2 emissions tax and the increase in the energy price

A contribution to support decision making in energy/water sypply chain optimisation

The seeking of process sustainability forces enterprises to change their operations. Additionally, the industrial globalization implies a very dynamic market that, among other issues, promotes the enterprises competition. Therefore, the efficient control and use of their Key Performance Indicators, including profitability, cost reduction, demand satisfaction and environmental impact associated to the development of new products, is a significant challenge. All the above indicators can be efficiently controlled through the Supply Chain Management. Thus, companies work towards the optimization of their individual operations under competitive environments taking advantage of the flexibility provided by the virtually inexistent world market restrictions. This is achieved by the coordination of the resource flows, across all the entities and echelons belonging to the system network. Nevertheless, such coordination is significantly complicated if considering the presence of uncertainty and even more if seeking for a win-win outcome. The purpose of this thesis is extending the current decision making strategies to expedite these tasks in industrial processes. Such a contribution is based on the development of efficient mathematical models that allows coordinating large amount of information synchronizing the production and distribution tasks in terms of economic, environmental and social criteria. This thesis starts presents an overview of the requirements of sustainable production processes, describing and analyzing the current methods and tools used and identifying the most relevant open issues. All the above is always within the framework of Process System Engineering literature. The second part of this thesis is focused in stressing the current Multi-Objective solution strategies. During this part, first explores how the profitability of the Supply Chain can be enhanced by considering simultaneously multiple objectives under demand uncertainties. Particularly, solution frameworks have been proposed in which different multi-criteria decision making strategies have been combined with stochastic approaches. Furthermore, additional performance indicators (including financial and operational ones) have been included in the same solution framework to evaluate its capabilities. This framework was also applied to decentralized supply chains problems in order to explore its capabilities to produce solution that improves the performances of each one of the SC entities simultaneously. Consequently, a new generalized mathematical formulation which integrates many performance indicators in the production process within a supply chain is efficiently solved. Afterwards, the third part of the thesis extends the proposed solution framework to address the uncertainty management. Particularly, the consideration of different types and sources of uncertainty (e.g. external and internal ones) where considered, through the implementation of preventive approaches. This part also explores the use of solution strategies that efficiently selects the number of scenarios that represent the uncertainty conditions. Finally, the importance and effect of each uncertainty source over the process performance is detailed analyzed through the use of surrogate models that promote the sensitivity analysis of those uncertainties. The third part of this thesis is focused on the integration of the above multi-objective and uncertainty approaches for the optimization of a sustainable Supply Chain. Besides the integration of different solution approaches, this part also considers the integration of hierarchical decision levels, by the exploitation of mathematical models that assess the consequences of considering simultaneously design and planning decisions under centralized and decentralized Supply Chains. Finally, the last part of this thesis provides the final conclusions and further work to be developed.La globalización industrial genera un ambiente dinámico en los mercados que, entre otras cosas, promueve la competencia entre corporaciones. Por lo tanto, el uso eficiente de las los indicadores de rendimiento, incluyendo rentabilidad, satisfacción de la demanda y en general el impacto ambiental, representa un area de oportunidad importante. El control de estos indicadores tiene un efecto positivo si se combinan con la gestión de cadena de suministro. Por lo tanto, las compañías buscan definir sus operaciones para permanecer activas dentro de un ambiente competitivo, tomando en cuenta las restricciones en el mercado mundial. Lo anterior puede ser logrado mediante la coordinación de los flujos de recursos a través de todas las entidades y escalones pertenecientes a la red del sistema. Sin embargo, dicha coordinación se complica significativamente si se quiere considerar la presencia de incertidumbre, y aún más, si se busca exclusivamente un ganar-ganar. El propósito de esta tesis es extender el alcance de las estrategias de toma de decisiones con el fin de facilitar estas tareas dentro de procesos industriales. Estas contribuciones se basan en el desarrollo de modelos matemáticos eficientes que permitan coordinar grandes cantidades de información sincronizando las tareas de producción y distribución en términos económicos, ambientales y sociales. Esta tesis inicia presentando una visión global de los requerimientos de un proceso de producción sostenible, describiendo y analizando los métodos y herramientas actuales así como identificando las áreas de oportunidad más relevantes dentro del marco de ingeniería de procesos La segunda parte se enfoca en enfatizar las capacidades de las estrategias de solución multi-objetivo, durante la cual, se explora el mejoramiento de la rentabilidad de la cadena de suministro considerando múltiples objetivos bajo incertidumbres en la demanda. Particularmente, diferentes marcos de solución han sido propuestos en los que varias estrategias de toma de decisión multi-criterio han sido combinadas con aproximaciones estocásticas. Por otra parte, indicadores de rendimiento (incluyendo financiero y operacional) han sido incluidos en el mismo marco de solución para evaluar sus capacidades. Este marco fue aplicado también a problemas de cadenas de suministro descentralizados con el fin de explorar sus capacidades de producir soluciones que mejoran simultáneamente el rendimiento para cada uno de las entidades dentro de la cadena de suministro. Consecuentemente, una nueva formulación que integra varios indicadores de rendimiento en los procesos de producción fue propuesta y validada. La tercera parte de la tesis extiende el marco de solución propuesto para abordar el manejo de incertidumbres. Particularmente, la consideración de diferentes tipos y fuentes de incertidumbre (p.ej. externos e internos) fueron considerados, mediante la implementación de aproximaciones preventivas. Esta parte también explora el uso de estrategias de solución que elige eficientemente el número de escenarios necesario que representan las condiciones inciertas. Finalmente, la importancia y efecto de cada una de las fuentes de incertidumbre sobre el rendimiento del proceso es analizado en detalle mediante el uso de meta modelos que promueven el análisis de sensibilidad de dichas incertidumbres. La tercera parte de esta tesis se enfoca en la integración de las metodologías de multi-objetivo e incertidumbre anteriormente expuestas para la optimización de cadenas de suministro sostenibles. Además de la integración de diferentes métodos. Esta parte también considera la integración de diferentes niveles jerárquicos de decisión, mediante el aprovechamiento de modelos matemáticos que evalúan lasconsecuencias de considerar simultáneamente las decisiones de diseño y planeación de una cadena de suministro centralizada y descentralizada. La parte final de la tesis detalla las conclusiones y el trabajo a futuro necesario sobre esta línea de investigaciónPostprint (published version

Green Logistics Oriented Framework for the Integrated Scheduling of Production and Distribution Networks - A Case of the Batch Process Industry

Nowadays, most consumable goods are produced and transported in batches. Within the globalized environment, the flow of these batches is raising dramatically to satisfy the recurrent demands of the increasing population. Planning the flow of these batches from suppliers to customers, through dynamic logistics systems, has a high degree of uncertainties on supply chain related decisions. In order to respond effectively and efficiently to these uncertainties, the supply chain network has to be redesigned, considering the economic and environmental requirements. To handle these requirements sustainably, green logistics is a promising approach. However, there is a lack of green logistics models which integrate both the production and distribution decisions within the batch process industries. This research develops a green logistics oriented framework in the case of the batch process industry. The framework integrates the tactical and operational levels of planning and scheduling to generate the optimum production and distribution decisions. A two-stage stochastic programming model is formulated to design and manage batch supply chain. This is a mixed-integer linear program of the two-stage stochastic production-distribution model with economic-environmental objectives. The first stage is concerned with optimum schedules of the production and distribution of the required batches. The second stage subsequently generates the optimum delivering velocities for the optimal distribution routes which are resulted from the first stage. Carbon emissions under uncertainties are incorporated as a function of random delivery velocities at different distribution routes within the network of the supply chain. To examine the applicability of the developed framework, the model is verified and validated through four theoretical scenarios as well as two real world case studies of multi-national batch process industries. The results of the analysis provide some insights results into supply chain costs and emissions. Based on the results, savings of about 43 percent of the total related economic and environmental costs were achieved compared to the actual situation at the case study companies. Cost savings mean long-term profitability, which is essential to sustain a worldwide competitive advantage. Furthermore, the stochastic and expected value solutions are compared in several scenarios. The stochastic solutions are consistently better with respect to costs and emissions. Calculations indicate that up to 13 percent of total cost savings are achieved when a stochastic approach is used to solve the problem as opposed to an expected value approach. The proposed framework supports academic green logistics models and real world supply chain decision making in batch process industry. Building such a framework provides a practical tool which links being green and being economically successful

Strategic analysis and optimization of bioethanol supply chains

In modern times, the interest in renewable energy has been increasing considerably in response to the growing energy demand and to the simultaneous concern about global warming effects. The urgency of this issue is related to dissociation between the perspective of a steady growth in demand for fuel and its supply, which is projected to become ever more uncertain and expensive. The phenomenon of climate change is widely recognized as a consequence of the increased concentration of greenhouse gases (GHG) in the atmosphere caused by anthropogenic activity, and to which the transport sector is a significant contributor. Among biofuels, biomass-based ethanol has been in a leading position for substituting petroleum-based road-fuels. Even if its actual carbon footprint is still debated, it is generally acknowledged a reduction in net GHG emissions with respect to oil. The complexity of the context discussed previously, guides us to the transition towards a more sustainable transport system which requires the adoption of effective quantitative tools able to encompassing the problem to the whole production chain (supply chain), that may help defining a more comprehensive view of biofuels. In dealing with such problems involving high decisional level, the analytical modelling is recognized as the best optimization option, particularly in the initial phase of design of unknown infrastructures in order to cope with a comprehensive management of production systems taking into account all supply chain stages. Mixed Integer Linear Programming (MILP) in particular, emerges as one of the most suitable tools in determining the optimal solutions of complex supply chain design problems where multiple alternatives are to be taken into account. In this sense, the multi-objective MILP (moMILP) enables simultaneous consideration of conflicting criteria (i.e., financial, environmental) to assist the decisions of interested parties on biofuels industry at strategic and tactical levels. Moreover, this complex analysis is addressed effectively by incorporating the principles of Life Cycle Analysis (LCA) within supply chain analysis techniques aiming at a quantitative assessment of the environmental burdens of each supply chain stage. Accordingly, the main purpose of the research presented in this Thesis is to cover this gap of knowledge in the literature. In the context of the development and adoption of bioenergy systems, the overall objective of this work is to provide quantitative and deterministic tools to analyze and optimize the supply chain as whole, to thereby identify the most suitable and feasible strategies for the development of future road transport systems. In this sense, the research design for this Thesis begins with the development and analysis of a multi-period moMILP modelling framework for the design and the optimization of bioethanol supply chain where economics and environmental sustainability (GHG emissions reductions potential) for first generation ethanol is addressed, considering possibilities of several technologies integration (including biogas production). Then, the analysis is focused on the general interactions of market policies under the European Emission Trading System in order to enhance the bioethanol market development trends to boost sustainable production of bioethanol. Next, a comprehensive modelling analysis to predict commodity price evolution dynamics and to extend the price forecasts to other goods related to bioethanol production is addressed. An assessment of the impact on the supply chain design of the recent proposed by the European Commission to amend the existing Directive in terms of accountability technique for biofuels is analyzed and discussed. Besides, multi-criteria decision making tools to support strategic design and planning on biofuel supply chains including several Game Theory features are evaluated. Finally to close up, the main achievements of the Thesis are exposed as well as the main shortfalls and possible future research lines are outlined. Models capabilities in steering decisions on investments for bioenergy systems are evaluated in addressing real world case studies referring to the emerging bioethanol production in Northern Italy