Petroleum refining processes provide the daily requirements of energy for the global market. Each refining process produces wastes that have the capacity to harm the environment if not properly disposed of. The treatment of refinery waste is one of the most complex issues faced by refinery managers. Also, the hazardous nature of these wastes makes them rather costly to dispose of for the refineries. In this thesis, system analysis tools are used to design a program that allows for the selection of the optimal control, minimization and treating options for petroleum refinery waste streams. The performance of the developed model is demonstrated via a case study. Optimal mitigation alternatives to meet the emission reduction targets were studied by evaluating their relative impact on the profitable operation of the given facility. It was found that the optimal mitigation steps was to reduce emission precursors by conducting feed switches at the refinery. In all cases, the optimal solution did not include a capital expansion of the emission control facilities and equipment.

The process industries are characterized by the significant consumption of fresh resources. This is a critical issue, which calls for an effective strategy towards more sustainable operations. One approach that favors sustainability and resource conservation is material recycle and/or reuse. In this regard, an integrated framework is an essential element in sustainable development. An effective reuse strategy must consider the process as a whole and develop plant-wide strategies. While the role of mass and energy integration has been acknowledged as a holistic basis for sustainable design, it is worth noting that there are many design problems that are driven by properties or functionalities of the streams and not by their chemical constituency. In this dissertation, the notion of componentless design, which was introduced by Shelley and El-Halwagi in 2000, was employed to identify optimal strategies for resource conservation, material substitution, and overall process integration. First, the focus was given on the problem of identifying rigorous targets for material reuse in property-based applications by introducing a new property-based pinch analysis and visualization technique. Next, a non-iterative, property-based algebraic technique, which aims at determining rigorous targets of the process performance in materialrecycle networks, was developed. Further, a new property-based procedure for determining optimal process modifications on a property cluster diagram to optimize the allocation of process resources and minimize waste discharge was also discussed. In addition, material substitution strategies were considered for optimizing both the process and the fresh properties. In this direction, a new process design and molecular synthesis methodology was evolved by using the componentless property-cluster domain and Group Contribution Methods (GCM) as key tools in developing a generic framework and systematic approach to the problem of simultaneous process and molecular design.

Petroleum refining processes provide the daily requirements of energy for the global market. Each refining process produces wastes that have the capacity to harm the environment if not properly disposed of. The treatment of refinery waste is one of the most complex issues faced by refinery managers. Also, the hazardous nature of these wastes makes them rather costly to dispose of for the refineries. In this thesis, system analysis tools are used to design a program that allows for the selection of the optimal control, minimization and treating options for petroleum refinery waste streams. The performance of the developed model is demonstrated via a case study. Optimal mitigation alternatives to meet the emission reduction targets were studied by evaluating their relative impact on the profitable operation of the given facility. It was found that the optimal mitigation steps was to reduce emission precursors by conducting feed switches at the refinery. In all cases, the optimal solution did not include a capital expansion of the emission control facilities and equipment.

The thesis is focused of finding a suitable space for set up and apply a new form of electronic auction and introduction a system of suppliers evaluation to the company UPC Czech republic, Ltd. These touls should lead to optimize the whole purchasing process of the company. The thesis included performance of methods and techniques to successfully management of selected activities in the organization and contains the description of all the current purchasing process.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The sector of small industrial companies has a problem with not being able to satisfy total market demand, mainly due to factories not capitalizing on their production capacity. Consequently, a method to solve downtime related problems has been proposed, which consists of applying 5S to reduce dead time stemming from disorder and uncleanliness, while simultaneously applying value stream mapping (VSM) to find the most efficient method to conduct processes. In conclusion, by implementing these tools in this manner, it was proven that businesses in this sector could operate with up to 73% efficiency.

This dissertation includes two process modeling studies -- (1) predictive modeling of large-scale integrated refinery reaction and fractionation systems from plant data – hydrocracking process; and (2) integrated process modeling and product design of biodiesel manufacturing. \r\n1. Predictive Modeling of Large-Scale Integrated Refinery Reaction and Fractionation Systems from Plant Data -- Hydrocracking Processes: This work represents a workflow to develop, validate and apply a predictive model for rating and optimization of large-scale integrated refinery reaction and fractionation systems from plant data. We demonstrate the workflow with two commercial processes -- medium-pressure hydrocracking unit with a feed capacity of 1 million ton per year and high-pressure hydrocracking unit with a feed capacity of 2 million ton per year in the Asia Pacific. This work represents the detailed procedure for data acquisition to ensure accurate mass balances, and for implementing the workflow using Excel spreadsheets and a commercial software tool, Aspen HYSYS from Aspen Technology, Inc. The workflow includes special tools to facilitate an accurate transition from lumped kinetic components used in reactor modeling to the boiling point based pseudo-components required in the rigorous tray-by-tray distillation simulation. Two to three months of plant data are used to validate models' predictability. The resulting models accurately predict unit performance, product yields, and fuel properties from the corresponding operating conditions.\r\n2. Integrated Process Modeling and Product Design of Biodiesel Manufacturing: This work represents first a comprehensive review of published literature pertaining to developing an integrated process modeling and product design of biodiesel manufacturing, and identifies those deficient areas for further development. It also represents new modeling tools and a methodology for the integrated process modeling and product design of an entire biodiesel manufacturing train. We demonstrate the methodology by simulating an integrated process to predict reactor and \r\nseparator performance, stream conditions, and product qualities with different feedstocks. The results show that the methodology is effective not only for the rating and optimization of an existing biodiesel manufacturing, and but also for the design of a new process to produce biodiesel with specified fuel properties. / Ph. D.

"Heat treating is the controlled heating and cooling of a material to achieve certain mechanical properties, such as hardness, strength and the reduction of residual stresses. Many heat treating processes require the precise control of temperature over the heating cycle. Typically, the energy used for process heating accounts for 2% to 15% of the total production cost. The objective of this work is to develop a comprehensive furnace model by improving the current Computerized Heat Treatment Planning System (CHT) based furnace model to accurately simulate the thermal profile of load inside the furnace. The research methodology was based on both experimental work and theoretical developments including modeling different types of heat treat furnaces. More than 50 experimental validations through case studies using the current CHT model were conducted in 11 manufacturing locations to identify the specific problems in the current model. An enhanced furnace model based on Knowledge Data Discovery (KDD) technique and neural network is developed and validated. The new model takes into account the real time furnace parameters determined from the experimental data and accounts for furnace deterioration and some of the complex gradients and heating patterns that exist inside the furnace that is difficult to model. "

The overwhelming majority of global trade is executed over maritime infrastructure, and port-side optimization problems are significant given that commercial maritime ports are hubs at which sea trade routes and land/rail trade routes converge. Therefore, optimizing maritime operations brings the promise of improvements with global impact. Major performance bottlenecks in maritime trade process include the handling of insurance claims on shipping containers and vessel service time at port. The former has high input dimensionality and includes data pertaining to environmental and human attributes, as well as operational attributes such as the weight balance of a shipping container; and therefore lends itself to multiple classification method- ologies, many of which are explored in this work. In order to compare their performance, a first-of-its-kind dataset was developed with carefully curated attributes. The performance of these methodologies was improved by exploring metalearning techniques to improve the collective performance of a subset of these classifiers. The latter problem formulated as a schedule optimization, solved with a fuzzy system to control port-side resource deployment; whose parameters are optimized by a multi-objective evolutionary algorithm which outperforms current industry practice (as mined from real-world data). This methodology has been applied to multiple ports across the globe to demonstrate its generalizability, and improves upon current industry practice even with synthetically increased vessel traffic.

Much research in the area of process integration has focused on steady-state processes. However, there are many process units that are inherently unsteady-state or perform best when operated in an unsteady-state manner. Unsteady-state units are vital to chemical processes but are unable to be included in current process optimization methods. Previous methods to optimize processes containing unsteady-state units place restrictions or constraints on their use. This optimization still does not give the best system design because the solution found will only be the best out of the available options which likely excludes the true optimal design. To remedy this, a methodology was created to incorporate unsteady-state process units into process optimization analysis. This methodology is as general as possible. Unlike many existing unsteadystate optimization methods, it determines all three main components of process design: the network configuration, sizes of units, and operation schedule. This generality ensures that the truly optimal process design will be found. Three problems were solved to illustrate the solution methodology. First, a general mass exchange network was optimized. The optimization formulation resulted in a mixed-integer nonlinear program, and linearization techniques were used to find the global solution. A property interception network was also optimized, the first work done using property integration for systems with unsteady-state behavior. Finally, an industrial semi-batch water purification system was optimized. This problem showed how process integration could be used to optimize a hybrid system and gain insights into the process under many different operating conditions.

With the escalating levels of water demand, there is a need for expansion in the capacity of water desalination infrastructure and for better management and distribution of water resources. This dissertation introduces a systems approach to the optimization of macroscopic water desalination and distribution networks to tackle three problems: 1. Optimal design of desalination and allocation networks for a given demand, 2. Optimal operation of an existing infrastructure of water desalination, distribution, and storage, 3.Optimal planning for expanding the capacity of desalination plants to meet an increasing water demand over a time horizon. A source-interception-sink representation was developed to embed potential configurations of interest. Mathematical programming was used to model the problem by studying different objective functions while accounting for constraints the supply, demand, mass conservation, technical performance, and economic aspects. Such approach determines the type of technologies to be selected, the location and capacity of the desalination plants, and the distribution of the desalinated water from sources to destinations. For the operation and planning problems, the planning horizon was discretized into periods and a multi-period optimization approach was adopted with decisions made for each period. Short- and long-term water storage options (e.g., in storage tanks, aquifers) were included in the optimization approach. Water recycle/reuse was enhanced via the use of treated water and its utilization was improved by minimizing the losses observed in discharged water resulting from the linkage of power plants and thermal desalination plants and the lack of integration between water production and consumption. Several case studies were solved to demonstrate the applicability of the devised approaches.