Strategic development in the petrochemical industry

Imperial Users onl

Medium-term optimization-based approach for the integration of production planning, scheduling and maintenance

A medium-term optimization-based approach is proposed for the integration of production planning, scheduling and maintenance. The problem presented in this work considers a multiproduct single-stage batch process plant with parallel units and limited resources. An MILP continuous-time formulation is developed based on the main ideas of travelling salesman problem and precedence-based constraints to deal with, sequence-dependent unit performance decay, flexible recovery operations, resource availability and product lifetime. Small scheduling examples have been solved and compared with adapted formulations from the literature, based on discrete-time and global-time events, demonstrating the effectiveness of the proposed solution approach. Additional planning and scheduling problems have been proposed by considering several time periods. Multi-period examples have been efficiently solved by the model showing the applicability of the solution approach for medium-size problems

Optimal control methodologies for the optimisation of maintenance scheduling and production in processes using decaying catalysts

In this thesis, optimal control methodologies are developed for solving problems involving the optimisation of maintenance scheduling and production in processes using decaying catalysts. Previously, such problems were solved using a category of methods which involve making decisions of discrete as well as continuous nature, called mixed-integer optimisation techniques. However, these techniques are combinatorial in nature and can solve differential equations only by approximations as collections of steady state equality constraints, and such features can cause these techniques difficulties in obtaining optimal and accurate solutions for these problems. The goal behind developing optimal control methodologies is to effectively solve these problems while overcoming the drawbacks that mixed-integer optimisation techniques face or would face in solving these problems. First, an optimal control methodology is developed to optimise maintenance scheduling and production in a process containing a reactor using decaying catalysts. This methodology involves using a multistage mixed-integer optimal control problem (MSMIOCP) formulation and obtaining solutions as a standard nonlinear optimisation problem, without using mixed-integer optimisation techniques. Two different solution implementations are required, each which has its own relative advantages. The methodology using the second procedure is particularly successful in effectively obtaining solutions within the stipulated tolerances. Further, the methodology possesses features of robustness because it enables a relatively small problem size, reliability because it solves differential equations using state-of-the-art integrators, and efficiency because it is not combinatorial in nature. These features indicate the methodology’s success in overcoming the drawbacks of using mixed-integer optimisation techniques to solve this problem. Next, the abovementioned methodology is extended to form an optimal control methodology to optimise maintenance scheduling and production in a process containing parallel lines of reactors using decaying catalysts. This methodology, when applied to a case study of such a process, is also able to effectively obtain solutions within the stipulated tolerances. Further, the solutions obtained, once again, possess features of robustness, reliability and efficiency, which indicate that the methodology can overcome the drawbacks that mixed-integer optimisation techniques would face, if used to solve such problems. And lastly, an optimal control methodology is developed for considering uncertainties in kinetic parameters in the optimisation of maintenance scheduling and production in a process containing a reactor using decaying catalysts. The methodology involves using a multiple scenario approach to consider parametric uncertainties and formulating a stochastic MSMIOCP, which is solved as a standard nonlinear optimisation problem as per the previously developed procedure. The results obtained provide insights into the effects of parametric uncertainties and the number of scenarios generated on the optimal operations, and indicate that the methodology is capable of solving this problem. Further, the robust, reliable and efficient nature of the results obtained suggest that the methodology can overcome the disadvantages that mixed-integer methods would introduce in the conventional methodologies, if such methodologies are used to solve such problems.Cambridge India Ramanujan Scholarship awarded by the Cambridge Trust and the Science and Engineering Research Board of Indi

Analysis of Fuel Reduction Strategies for Crude Distillation Unit

There is greater awareness today on the depleting fossil energy resources and the growing problem of atmospheric pollution. Engineers are developing practical techniques to ensure energy processes are designed and operated efficiently. Inefficient furnaces and heat exchangers contribute to the problem due to higher fuel demand and higher carbon emission. In crude preheat train (CPT), fouling causes the reduction of heat transfer efficiency, which leads to higher furnace fuel consumption, and exert additional cost for heat exchanger cleaning and lost production. This thesis presents strategies to reduce fuel consumption in the furnace, which will lead to reductions of operational cost and environmental emission. The method of exergy analysis is applied to determine the baseline energy efficiency of the furnace and CPT in a crude distillation unit (CDU). The strategies consist of locating and reducing exergy lost through process modifications of the energy system and developing optimum scheduling for retrofit and/or cleaning of heat exchangers in the CPT. There are two options for achieving fuel savings in the furnace. The options are reduction of heat lost from furnace stack and enhancement of heat recovery in the CPT. The second option involves plant shutdown for overall cleaning of CPT (Case 1), online cleaning of heat exchangers (Case 2) and combined online cleaning with retrofit of high efficiency heat exchangers (Case 3). Reduction of heat loss from furnace stack contributes to the smallest cost saving of 6.44% without carbon credit. With carbon credit, the saving is increased to 6.70%. The largest energy and carbon dioxide emission savings are found from Case 3. The installation of high efficiency heat exchangers improves furnace inlet temperature (FIT) from 215oC to 227oC. Furthermore, Case 3 results in the highest percentage of cost saving by about 71% and 62% with and without carbon credit, respectively. The payback period for investment in high efficiency heat exchangers is 3 months, with carbon credit, and 4 months, without carbon credit, respectively. Thus, Case 3 is the most cost effective option for reductions of energy consumption and carbon dioxide emission in the CDU

固体触媒によるエタンからエチレン及び芳香族炭化水素への転換

早大学位記番号:新8407早稲田大

Process optimization under uncertainty

The ability of a production plant to be flexible by adjusting the operating conditions to changing demands, prices of the products and the raw materials is crucial to maintain a profitable operation. In this respect, the application of mathematical optimization techniques is unanimously recognized to be successful to improve the decision-making process. Typical examples are production planning, scheduling, real-time optimization and advanced process control. The more information are available to the optimization approach, the more "optimal" are the resulting decisions: the "optimal" production strategy cannot reduce the inventory costs if no supply-chain model is integrated into the production planning optimization. This thesis lies in the context of Enterprise-wide optimization with the goal of integrating decision layers and functions while accounting for uncertain information. A stochastic programming approach is adopted to integrate production scheduling with energy management and production planning with predictive maintenance. The approaches are analysed from a formulation perspective and from a computational point of view, which is necessary to deal with one of the challenges of the presented methods consisting in the size of the resulting optimization problems. To reduce the electricity cost that is generated by the uncertain peaks of the dayahead price, a two-stage risk-averse optimization is proposed to simultaneously define the optimal bidding curves for the day-ahead market and the optimal production schedule. The large-scale MILP problem is solved with a scenario-based decomposition technique, the progressive hedging algorithm. Heuristic procedures are applied to speed up the solution phase and to avoid the oscillatory behaviour due to the integer variables. Since large electricity consumers rely on Time-Of-Use power contracts to handle the volatility of the day-ahead price, the two-stage formulation is expanded into a multi-stage optimization to optimally purchase electricity from different sources and to generate electric power with a power plant. The unpractical size of the resulting problem is handled by approximating the multi-stage tree with a series of two-stage scenario-trees within a rolling horizon procedure. A mixed time grid handles the multi-scale nature of the problem by making short-term decisions with a detailed model and catching their effect on the long-term future with an aggregated model. While the electricity prices introduce exogenous uncertain information into the optimization problem, the predictive maintenance optimization carries endogenous uncertain sources into the production planning problem. Endogenous uncertainties, contrary to the exogenous ones, are uncertain information that can be modified (in the probability or in the timing of the realization) by the decision maker. The prognosis technique of the Cox model is embedded into a multi-stage stochastic program to consider an uncertain Remaining Useful Life of the equipment when the optimal operating conditions of the plant are defined. Two modelling approaches (based on superstructure-scenario trees and on conditional non-anticipativity constraints) are proposed to formulate the optimization problem with endogenous uncertainties. Two Benders-like decomposition techniques and several branching priority schemes are applied to handle the high complexity of the resulting optimization problems

Electrodos de (Ni,Mo)-TiO2 para pilhas de combustiveis de óxido sólido reversiveis

One of the major issues of the renewable energies is their dependence of the weather conditions. If those are favourable, the energy will be produced in excess and wasted, due to limited capacity of the existing storage technologies. On the contrary, if the weather conditions are unfavourable, the energy produced will be insufficient, thus requiring the consumption of fossils fuels to assure a constant electrical output. The reversible solid oxide fuel cells (RSOFC) can contribute to the solution of this problem, provided by capability to operate in two different modes, with the hydrogen or carbon as the energy carrier. In one mode, the electricity is produced by the transformation of hydrogen into water, this is to be used when the weather conditions are unfavourable. In reverse operation mode, hydrogen is produced from the water using the excess of electricity produced by renewable energies, when the conditions are favourable. Although being fairly efficient and highly promising, this technology is still not widely available due to high electrode degradation, expensive catalytic materials, low resistance to carbon deposition and difficulties of the hydrogen storage, among others. This work was initially focusing on development of a composite material for fuel electrodes of Reversible Solid Oxide Fuel Cells (RSOFC). Base on the literature review, the nominal NiTiO3 (reference), NiTiO3 (90% mol.)- MoO3 (10% mol.) and NiTiO3 (80% mol.)- MoO3 (20% mol.) compositions were selected, prepared and characterized in the conditions, relevant for RSOFC operation. The electrical measurements combined with structural studies revealed fast degradation due to oxidation even at low temperatures, rendering their application problematic for RSOFC technology. Additional assessment of the prepared materials as electrocatalysts for alkaline water electrolysis was performed and demonstrated their potential applicability for boosting hydrogen evolution process if the applied cathodic polarization is sufficient to prevent oxidation of the Magnéli phases and decomposition of metallic phases, or redox cycling can be applied to recover the electrode surface. The obtained experimental results were correlated with thermodynamic predictions made by calculation of Ellingham and Pourbaix diagrams.Um dos maiores problemas das energias renováveis é a sua dependência das condições climatéricas. Caso estas sejam favoráveis, será produzido excesso de energia sendo esta desperdiçada devido à baixa capacidade de armazenamento por parte da tecnologia disponível. Caso contrário, se as condições climatéricas forem adversas, a energia produzida será insuficiente, sendo necessário recorrer ao consumo dos combustíveis fosseis para assegurar uma produção constante de eletricidade. As pilhas de combustível de oxido sólido reversíveis (PCOSR) podem contribuir para a resolução deste problema, devido a sua capacidade de operar em dois modos diferentes, nos quais o hidrogénio ou o carbono atuam como unidades para o armazenamento de energia. Num dos modos, a eletricidade é produzida pela transformação do hidrogénio em água, este deverá ser utilizado aquando as condições climatéricas forem adversas. No outro, o hidrogénio é produzido através da água através da utilização do excesso elétrico produzido pelas energias renovais, aquando as condições forem favoráveis. Apesar de ser relativamente eficiente e muito promissor, esta tecnologia não se encontra amplamente disponível, devido à elevada degradação dos elétrodos, dos materiais catalíticos serem dispendiosos, baixa resistência à deposição de carvão e dificuldades no armazenamento do hidrogénio, entre outros. Inicialmente, este trabalho foi focado no desenvolvimento de um material compósito para ser aplicado como elétrodo de combustível em Pilhas de Combustível de Óxido Sólido Reversíveis (PCOSR). Baseado na revisão bibliográfica, os NiTiO3 (referência), NiTiO3 (90% mol.) – MoO3 (10 % mol.) e NiTiO3 (80% mol.) – MoO3 (20% mol.) compósitos foram selecionados, preparados e caracterizados em condições conformes à da operação das PCOSR. As medidas elétricas assim como os estudos estruturais revelaram uma rápida degradação devido à oxidação, mesmo a baixa temperatura, evidenciando que a sua aplicação nas PCOSR será problemática. Adicionalmente, os materiais preparados foram avaliados como electro catalisadores para a eletrolise de água alcalina, sendo demonstrado a sua potencial aplicabilidade para favorecer o processo de evolução do hidrogénio, aquando a polarização catódica aplicada for suficiente para prevenir a oxidação das fases de Magnéli assim como a decomposição da fase metálica, ou ciclos de redox poderão ser aplicados para recuperar a superfície dos elétrodos. Os dados obtidos experimentalmente foram relacionados com predições termodinâmicas pelo cálculo de diagramas de Ellingham e Pourbaix.Mestrado em Engenharia de Materiai

An Optimization Approach for Integrating Planning and CO2 Mitigation in the Power and Refinery Sectors

Climate change is one of the greatest and probably most challenging environmental, social and economical threats facing the world this century. Human activities have altered the chemical composition of the atmosphere through the buildup of significant quantities of greenhouse gases (GHGs), which remain in the atmosphere for long periods of time and intensify the natural greenhouse effect. Increasing concentrations of greenhouse gases, mainly CO2, are likely to accelerate the rate of climate change. Concerns are growing about how increases in CO2 caused by human activities are contributing to the natural greenhouse effect and raising the Earth's average temperature. Electricity generation, especially from fossil fuel, and petroleum industries contribute the most to greenhouse gases emissions in Canada. As of 2004, they contributed to about 37% of total (GHGs). Risks of climate change and subsequent future environmental regulations are pressing electricity and petroleum refining industries to minimize their greenhouse gas emissions, mainly CO2. Fossil fuel power plants and refineries are now being challenged to comply with the Kyoto protocol by the United Nations Framework Convention and Climate Change (UNFCC). Canada’s target is a reduction in CO2 emissions of 6% from 1990 level. In this thesis, an optimization approach for integrating planning and CO2 reduction is developed for electricity and refinery sectors. Three different CO2 mitigation options are considered in each case. For the electricity sector, these mitigation options were 1) fuel balancing (optimal adjustment of the operation of existing generating stations to reduce CO2 emissions without making structural changes to the fleet), 2) fuel switching (switching from carbon intensive fuel to less carbon intensive fuel, essentially switching from coal to natural gas) and 3) implementing different technologies for efficiency improvement. The optimization model takes into account meeting electricity demand and achieving a certain CO2 reduction target at a minimum overall cost. The model was formulated as a Mixed Integer Non Linear Program (MINLP) and was implemented in GAMS (General Algebraic Modeling System). Exact linearization techniques were employed to facilitate solution development. The computer program was capable of determining the best strategy or mix of strategies to meet a certain CO2 reduction target at minimum cost. The model was illustrated on a case study for Ontario Power Generation (OPG) fleet. The results showed that for 1% CO2 reduction target, only fuel balancing need to be applied and even a decrease of about 1.3% in overall cost was obtained. The optimizer chose to increase production from all non fossil fuel power plants and to decrease production from natural gas power plant. This is because natural gas is the most expensive fuel that OPG uses. For higher reduction targets, it was necessary to implement fuel switching. For 30% reduction, for example, 11 boilers out of 27 (4 are already natural gas) are switched from coal to natural gas and the cost increases by about 13%. Applying efficiency improvement technologies such as installing new turbine blades was a good option only at small reduction targets. As the reduction target increases, the optimizer chose not to implement efficiency improvement technologies and only fuel switching was the best option to select in addition to fuel balancing. For the refinery sector, a similar strategy was applied. An optimization model was developed to maximize profit from selling final products and to meet a given CO2 reduction target with products demand and specifications. Three CO2 mitigation options were considered and these were: 1) balancing that implies the increase in production from units that emit less CO2 emissions provided that demand is met, 2) fuel switching that involves switching from current carbon intensive fuel to less carbon intensive fuel such as natural gas, 3) implementation of CO2 capture technologies. Chemical absorption (MEA) process was used as the capture process. Prior to the development of the refinery planning model, a sub-model was developed for each unit in a refinery layout. Then, the sub-models were integrated into a master planning model to meet final products demand and specifications with the objective of maximizing profit without CO2 mitigation options. The model was solved first as a Non Linear Program (NLP). Then, binary variables representing the existence or no existence of fuel switching option and CO2 capture processes were introduced into the model. The model was formulated as a Mixed Integer Non Linear Program (MINLP), coded in GAMS, and applied to different case studies. The results showed that the refinery planning model tends to produce more from the most profitable product, which is gasoline, and chose to blend products into the most profitable pool unless the demand needs to be satisfied for certain other products. The model, for example, chose to send kerosene from the diesel hydrotreater to the kerosene pool and not to the diesel pool since kerosene has higher selling value than diesel. When CO2 mitigation options were introduced into the model, only 0.4% CO2 reduction was achieved by simply decreasing production from the hydrocracker (HC) unit and increasing production from the fluidized catalytic cracking (FCC) unit. This was done because the FCC unit tends to emit less CO2 compared to the HC unit. At higher reduction target such as 1%, fuel switching was implemented by choosing the FCC to run with natural gas. The profit decreased slightly because of the retrofit cost of switching. It was noticed also that fuel switching can achieve a maximum of 30% reduction in CO2 emissions. This was achieved by switching all units to run with natural gas that emits less CO2 emissions. For a reduction target higher than 30%, CO2 capture technologies need to be applied. For 60% reduction, the optimization chose to switch three units (out of 8) and to capture CO2 emissions coming from four units. Only the FCC remained unchanged. A decrease in the profit was noticed as the reduction target increases since more units need to be switched and more CO2 need to be captured. The results showed that adding sequestration cost further decreased the profit. However, it was noticed that the selling price of final products had the most effect on the profit. An increase of 20%, for example, in final products’ prices, leads to a 10% increase in profit even when the CO2 reduction target was as high as 80%. When the retrofit cost for switching and capture was decreased by 30%, the effect on the profit was noticed only at higher reduction targets since more units were switched and more CO2 capture units were implemente