Batch Scheduling Using Matrix Approach Under Supply Change

Batch processing is the predominant mode of production operations for the low volume manufacturing of chemical, polymers and food products. Batch processing can be classified as single product batch process or multiple product batch process. Single product batch process in which single product is produce as compared to multiple product batch process where more than one product is produced using the same batch facility in successive campaigns. More recent works have considered the more complicated cases of processes in which each of the products has its own production sequence and make use of processing units in different combinations. In batch processmg, the profitability in economics lies heavily on the scheduling of the production sequence. Scheduling optimization normally aimed at minimizing the makespan (i.e. completion time of the batch process.), leading to overall optimization of the production cost. The complication in scheduling is amplified when the feed change is taken into account. Disruption of feed typically requires a large amount of time to generate an optimal schedule. The proposed approach to address these issues in order to optimize batch production uses matrix to represent the batch recipes which is then solved optimal makespan based on a selected sequence. The arrangement of the matrix rows is according to the best sequence based on the availability or the disruption of supply. The user is then provided with production sequence options based on process requirement and supply

Energy Storage and Green Hydrogen Systems in Electricity Markets: A Modelling and Optimization Framework with Degradation and Uncertainty Considerations

Mención Internacional en el título de doctorThe increasing penetration of renewable energy in electrical systems requires advances in increasing their controllability. Energy Storage Systems (ESSs) are one of the solutions, since they allow the management of generated energy. Green hydrogen production systems, on the other hand, can utilize electricity to produce hydrogen. This energy carrier which can be sold for revenue generation and can be produced using Alkaline Electrolyzers (AELs). To coordinate these systems in renewable energy plants, advanced control techniques are needed. Complex processes such as degradation, partial loading and the effect of uncertainties must be considered. These considerations add to the complexity, which can obstruct control process, hence a simplistic formulation is required. This dissertation addresses this issue by implementing the effect of both ESS and AEL degradation into short-term planning keeping a linear formulation. Moreover, electrolyzer partial loading effect and operational states are also considered. Novel approaches in their inclusion into short-term planning for electricity market participation are proposed, analyzing their long-term economical significance. Due to the nature of spot electricity markets, which require the commitment of energy delivery beforehand, the uncertainty of renewable source and electricity prices may affect the performance of the system. Various stochastic approaches for short-term optimization are evaluated, with the proposal of novel strategies. The long-term impact of including risk-aware strategies is also analyzed in a simulation framework, whose results indicate that conservative approaches do not necessarily yield better outcomes. The present study commences with the modelling and formulation of a standalone ESS participating in the day-ahead market. A renewable energy source is incorporated into this model, creating a Hybrid Farm (HF) for multi-market participation. Lastly, a green hydrogen production system is also integrated, allowing the involvement in the hydrogen market. A novel algorithm for operation under uncertainties is proposed, which has been found to outperform a classical Montecarlo approach. Throughout the research, Python was employed as the programming language of choice. The generated code has been uploaded to a public repository. Real historical data was used to validate the findings and provide a more realistic representation of the systems under study.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Mónica Chinchilla Sánchez.- Secretario: Joaquín Eloy-García Carrasco.- Vocal: Pedro Vicente Jover Rodrígue

Optimization Models for Biorefinery Supply Chain Network Design under Uncertainty

Biofuels are attracting increasing attention worldwide due to its environmental and economic benefits. The high levels of uncertainty in feedstock yield, market prices, production costs, and many other parameters are among the major challenges in this industry. This challenge has created an ongoing interest on studies considering different aspects of uncertainty in investment decisions of the biofuel industry. The Renewable Fuel Standard (RFS) sets policies and mandates to support the production and consumption of biofuels. However, the uncertainty associated with these policies and regulations of biofuel production and consumption have significant impacts on the biofuel supply chain network. The goal of this research is first to determine the optimal design of supply chain for biofuel refineries in order to maximize the annual profit considering uncertainties in fuel market price, feedstock yield and logistic costs. In order to deal with the stochastic nature of the parameters in the biofuel supply chain, we develop two-stage stochastic programming models in which Conditional Value at Risk (CVaR) is utilized as a risk measure to control the amount of shortage in demand zones. Two different approaches including the expected value and CVaR of the profit are considered as the objective function. This study also aims to investigate the impacts of the governmental policies and mandates on the total profit in the biofuel supply chain design problem. To achieve this goal, the two-stage stochastic programming models are developed in which conditional value at risk is considered as a risk measure to control the shortage of mandate. We apply these models for a case study of the biomass supply chain network in the state of Iowa to demonstrate the applicability and efficiency of the presented models, and assess the results

Strategic development in the petrochemical industry

Imperial Users onl

Handling Uncertainties in Process Optimization

Esta tesis doctoral presenta el estudio de técnicas que permiten manejar las incertidumbres en la optimización de procesos, desde el punto de vista del comportamiento aleatorio de las variables y de los errores en los modelos utilizados en la optimización. Para el tratamiento de las variables inciertas, se presenta la aplicación de la Programación de dos Etapas y Optimización Probabilística a un proceso de hidrodesulfuración. Los resultados permiten asegurar factibilidad en la operación, independiente del valor que tome la variable aleatoria dentro de su distribución de probabilidad. Acerca del manejo de la incertidumbre derivada del conocimiento parcial del proceso, se ha estudiado el método de Optimización en Tiempo Real con adaptación de modificadores, proponiendo mejoras que permiten: (1) evitar infactibilidades en su evolución, (2) obtener el óptimo real del proceso sin necesidad de estimar sus gradientes y (3) identificar las limitaciones para su aplicación en sistemas dinámicos de horizonteDepartamento de Ingeniería de Sistemas y Automátic