Dynamic Simulation Applied to Refinery Hydrogen Networks

[Abstract] This study analyses the usefulness of process network dynamics simulation for decision-making in refinery hydrogen networks. A theoretical hydrogen network of three desulphurisation plants is modelled, and three case scenarios discussed: baseline, high demand, and low demand. Discussion focuses on how the information from the simulation is interpreted and its usefulness for debottlenecking, scheduling and what-if analysis. Stress is put on dynamics of the system and their consequences in process operation throughout the network. Hydrogen purity is highlighted as the most affected variable, and discussed its network wide effect. In addition, the responses of inflows, outflows and headers are analysed. Although the model used is a simplified representation of the actual processes, the simulation analysis showed potential as decision-making support not provided with steady state models. Further researches based on real case-studies should be conducted to better conclude on the efficient usage of simulation in aiding refinery hydrogen networks operational decision

A novel framework for integrating real-time optimization and optimal scheduling : Application to heat and power systems

The optimization of heat and power systems operation is a complex task that involves continuous and discrete variables, operating and environmental constraints, uncertain prices and demands and transition constraints for startups or shutdowns. This work proposes a novel methodology for integrating scheduling optimization and real-time optimization (RTO) in order to face and solve such optimization problem. In a first stage, an offline optimization finds a scheduling for the whole horizon under study, which sets the startups and shutdowns of pieces of equipment with long transition times. A second stage solves a multiperiod RTO, which corrects the forecasts and adapts the model before optimiz-ing the process. Although the proposed methodology is illustrated through a case study consisting in a heat and power system, it can be generalized to other systems and processes. The obtained results show significant improvements in comparison with applying the results of a single offline scheduling optimization.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A novel framework for integrating real-time optimization and optimal scheduling : Application to heat and power systems

The optimization of heat and power systems operation is a complex task that involves continuous and discrete variables, operating and environmental constraints, uncertain prices and demands and transition constraints for startups or shutdowns. This work proposes a novel methodology for integrating scheduling optimization and real-time optimization (RTO) in order to face and solve such optimization problem. In a first stage, an offline optimization finds a scheduling for the whole horizon under study, which sets the startups and shutdowns of pieces of equipment with long transition times. A second stage solves a multiperiod RTO, which corrects the forecasts and adapts the model before optimiz-ing the process. Although the proposed methodology is illustrated through a case study consisting in a heat and power system, it can be generalized to other systems and processes. The obtained results show significant improvements in comparison with applying the results of a single offline scheduling optimization.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Operability Considerations for Retrofit Design of Industrial Process Energy Systems

Energy efficiency is crucial to reduce fuel usage and related emissions in industry.\ua0 In energy-intensive process industry, the use of heat accounts for a large share of the total energy use and a reduction of the heating and cooling demand is thus important for decreasing energy use. Reductions in heating and cooling demand can be achieved by increased heat integration through heat exchange within the industrial process. However, this often increases the number of process interconnections, which can lead to operability issues, which could potentially be a barrier for implementing the heat integration measures. To better estimate the potential for energy efficiency through heat integration and to enable the implementation of more heat integration measures, an open inventory mapping is needed to clarify which operability considerations are important to include in such analyses.This thesis presents an investigation of operability considerations for heat integrations retrofit proposals. The study is based on a theoretical framework, a qualitative evaluation and a model-based analysis of the consequences for operation of the process utility steam system. The theoretical framework was developed through a literature review and an analysis of possible operability effects through process implications resulting from increased heat integration. This framework was used to design heat exchanger network retrofit proposals that included selected operability issues at a case study oil refinery. The retrofit proposals were evaluated in an interview study with engineers at the oil refinery. The effect of the retrofit proposals on the steam system was analysed using a steam system model.The results indicate that it is valuable to take process aspects into consideration at an earlier design stage when designing heat exchanger network retrofits for increased heat integration. If operability, non-energy benefits, practical implementation issues and utility systems are considered in an early design stage, several issues can be avoided and large benefits could be achieved for the process

APPROXIMATION ASSISTED MULTIOBJECTIVE AND COLLABORATIVE ROBUST OPTIMIZATION UNDER INTERVAL UNCERTAINTY

Optimization of engineering systems under uncertainty often involves problems that have multiple objectives, constraints and subsystems. The main goal in these problems is to obtain solutions that are optimum and relatively insensitive to uncertainty. Such solutions are called robust optimum solutions. Two classes of such problems are considered in this dissertation. The first class involves Multi-Objective Robust Optimization (MORO) problems under interval uncertainty. In this class, an entire system optimization problem, which has multiple nonlinear objectives and constraints, is solved by a multiobjective optimizer at one level while robustness of trial alternatives generated by the optimizer is evaluated at the other level. This bi-level (or nested) MORO approach can become computationally prohibitive as the size of the problem grows. To address this difficulty, a new and improved MORO approach under interval uncertainty is developed. Unlike the previously reported bi-level MORO methods, the improved MORO performs robustness evaluation only for optimum solutions and uses this information to iteratively shrink the feasible domain and find the location of robust optimum solutions. Compared to the previous bi-level approach, the improved MORO significantly reduces the number of function calls needed to arrive at the solutions. To further improve the computational cost, the improved MORO is combined with an online approximation approach. This new approach is called Approximation-Assisted MORO or AA-MORO. The second class involves Multiobjective collaborative Robust Optimization (McRO) problems. In this class, an entire system optimization problem is decomposed hierarchically along user-defined domain specific boundaries into system optimization problem and several subsystem optimization subproblems. The dissertation presents a new Approximation-Assisted McRO (AA-McRO) approach under interval uncertainty. AA-McRO uses a single-objective optimization problem to coordinate all system and subsystem optimization problems in a Collaborative Optimization (CO) framework. The approach converts the consistency constraints of CO into penalty terms which are integrated into the subsystem objective functions. In this way, AA-McRO is able to explore the design space and obtain optimum design solutions more efficiently compared to a previously reported McRO. Both AA-MORO and AA-McRO approaches are demonstrated with a variety of numerical and engineering optimization examples. It is found that the solutions from both approaches compare well with the previously reported approaches but require a significantly less computational cost. Finally, the AA-MORO has been used in the development of a decision support system for a refinery case study in order to facilitate the integration of engineering and business decisions using an agent-based approach

Otimização do balanço termelétrico de uma refinaria de petróleo: MILP x MINLP

This work presents two algebraic models to represent the thermoelectric system of an oil refinery, which are optimized by mathematical programming techniques. The first model is mixed integer linear programming (MILP), while the second is mixed integer nonlinear programming (MINLP), in a way that the main nonlinearities are the variations in boiler efficiency with regrets to load and steam headers temperatures. The nonlinear models were optimized using different solvers (DICOPT, BONMIN, COUENNE and SCIP) and two initial points. The results of the optimizations of the models in different operating and cost scenarios allowed to verify that the MINLP model, optimized by the DICOPT and using the MILP model results as the starting point, was a solution with better results for optimization of the thermoelectric balance, reaching the lowest operating cost.Este trabalho desenvolve dois modelos algébricos para representar o sistema termelétrico de uma refinaria de petróleo, que são otimizados por técnicas de programação matemática. O primeiro modelo é de programação linear inteira mista (MILP), ao passo que o segundo é de programação não linear inteira mista (MINLP), tal que as principais não linearidades abordadas foram as curvas de eficiência das caldeiras em função da carga e temperatura dos coletores de vapor como variáveis de otimização. Os modelos não lineares foram otimizados utilizando diferentes solvers (DICOPT, BONMIN, COUENNE e SCIP) e dois pontos iniciais. Os resultados das otimizações dos modelos em diferentes cenários operacionais e de custos permitiram verificar que o modelo MINLP, otimizado pelo DICOPT e usando o resultado do modelo MILP como ponto inicial das variáveis, mostrou-se uma estratégia robusta, de baixo tempo de execução e de melhores resultados para otimização do balanço termelétrico, chegando ao menor custo operacional

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