32 research outputs found

    Dynamic economic and emission dispatch model considering wind power under Energy Market Reform: A case study

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    With the increasing issues in the environmental and the high requirement for energy, the Energy Market Reform (EMR) was introduced by the UK government. This paper develops a novel Dynamic Economic and Emission Dispatch (DEED) model for a combined conventional and wind power system incorporating the carbon price floor (CPF) and the Emission Performance Standard (EPS) that is supported by the EMR. The proposed model aims to determine the optimal operation strategy for the given system on power dispatch taking into account wind power waste and reserve and also the environmental aspect, especially the CPF of greenhouse gases and the emission limit of the EPS for different decarbonisation scenarios. Case studies for the demand profile in the Sheffield region in the UK with different time intervals is presented. The results indicate that renewable power is superior in both the economics and emissions to a mid to long-term energy strategy in the UK

    Intermodal Transfer Coordination in Logistic Networks

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    Increasing awareness that globalization and information technology affect the patterns of transport and logistic activities has increased interest in the integration of intermodal transport resources. There are many significant advantages provided by integration of multiple transport schedules, such as: (1) Eliminating direct routes connecting all origin-destinations pairs and concentrating cargos on major routes; (2) improving the utilization of existing transportation infrastructure; (3) reducing the requirements for warehouses and storage areas due to poor connections, and (4) reducing other impacts including traffic congestion, fuel consumption and emissions. This dissertation examines a series of optimization problems for transfer coordination in intermodal and intra-modal logistic networks. The first optimization model is developed for coordinating vehicle schedules and cargo transfers at freight terminals, in order to improve system operational efficiency. A mixed integer nonlinear programming problem (MINLP) within the studied multi-mode, multi-hub, and multi-commodity network is formulated and solved by using sequential quadratic programming (SQP), genetic algorithms (GA) and a hybrid GA-SQP heuristic algorithm. This is done primarily by optimizing service frequencies and slack times for system coordination, while also considering loading and unloading, storage and cargo processing operations at the transfer terminals. Through a series of case studies, the model has shown its ability to optimize service frequencies (or headways) and slack times based on given input information. The second model is developed for countering schedule disruptions within intermodal freight systems operating in time-dependent, stochastic and dynamic environments. When routine disruptions occur (e.g. traffic congestion, vehicle failures or demand fluctuations) in pre-planned intermodal timed-transfer systems, the proposed dispatching control method determines through an optimization process whether each ready outbound vehicle should be dispatched immediately or held waiting for some late incoming vehicles with connecting freight. An additional sub-model is developed to deal with the freight left over due to missed transfers. During the phases of disruption responses, alleviations and management, the proposed real-time control model may also consider the propagation of delays at further downstream terminals. For attenuating delay propagations, an integrated dispatching control model and an analysis of sensitivity to slack times are presented

    Computational Analysis and Design Optimization of Convective PCR Devices

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    Polymerase Chain Reaction (PCR) is a relatively novel technique to amplify a few copies of DNA to a detectable level. PCR has already become common in biomedical research, criminal forensics, molecular archaeology, and so on. Many have attempted to develop PCR devices in numerous types for the purpose of the lab-on-chip (LOC) or point-of-care (POC). To use PCR devices for POC lab testing, the price must be lower, and the performance should be comparable to the lab devices. For current practices with the existing methods, the price is pushed up higher partially due to too much dependence on numerous developmental experiments. Our proposition herein is that the computational methods can make it possible to design the device at lower cost and less time, and even improved performance. In the present dissertation, a convective PCR, that is the required flow circulation is driven by the buoyancy forces, is researched towards the use in POC testing. Computational Fluid Dynamics (CFD) is employed to solve the nonlinear equations for the conjugate momentum and heat transfer model and the species transport model. The first application of the models considers four reactors in contact with two separate heaters, but with different heights. Computational analyses are carried out to study the nature of buoyancy-driven flow for DNA amplification and the effect of the capillary heights on the performance. The reactor performance is quantified by the doubling time of DNA and the results are experimentally verified. The second application includes a novel design wherein a reactor is heated up by a single heater. A process is established for low-developmental cost and high-performance design. The best is searched for and found by evaluating the performance for all possible candidates. The third application focuses on the analysis of the performance of single-heater reactors affected by positions of a capillary tube: (1) horizontal, and (2) vertical. In the last application, numerous double-heater reactor designs are considered to find the one that assure the optimal performance. Artificial Neural Network (ANN) is employed to approximate the CFD results for optimization. In summary, through the four segments of our studies, the results show significant possibilities of increasing the performance and reducing the developmental cost and time. It is also demonstrated that the proposed methodology is advantageous for the development of cPCR reactors for the purpose of POC applications

    Combined economic and emission dispatch considering conventional and wind power generating units

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    Combined economic and emission dispatch (CEED) is an optimization solution to the short-term demand and supply balancing in the power network. Given that wind power is playing an increasing role in the UK, this paper develops a CEED model for a combined conventional and wind power system under the UK energy policies. The proposed model aims to determine the optimal operation strategy for the given system with the consideration of wind power curtailment and reservation and also the environmental aspect, especially the carbon price of greenhouse gases (GHG) and emission limits of decarbonisation scenarios. From two case studies, increasing the carbon price at a low emission limit leads to an increase in the total cost, but the rate of the increase is mitigated on decreasing the emission limits. Moreover, dispatch is dominated by the carbon price at high emission allowance levels and by the emission allowance at low emission allowances

    Diseño de un método selectivo inspirado en enfriamiento simulado aplicado a un proyecto bioquímico

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    Este trabajo describe un método inspirado en la estrategia de enfriamiento simulado para el caso de estimación de parámetros cinéticos de una reacción metabólica simple. El método se combina con un algoritmo genético que ya ofrece una combinación de parámetros válida, pero que debido a la naturaleza del modelo no consigue decantarse por una única solución, siendo ligeramente diferentes las soluciones en cada ejecución, aún obteniendo el mismo valor de la función de fitness. Con la propuesta formulada en este trabajo, se ha definido un criterio para analizar los resultados proporcionados por el algoritmo genético. Dicho criterio se basa en la minimización de una función de energía, que es propia del método de optimización global conocido como Enfriamiento Simulado. Esta función permite observar cómo evoluciona el modelo biológico hacia la estabilidad en función de los valores de mínima energía y según las diferentes combinaciones de parámetros proporcionadas por el algoritmo genético. Losresultados obtenidos muestran la eficacia del método propuesto. [ABSTRACT] This project document describes a method inspired in the strategy of simulated annealing for the estimation of kinetic parameters in a simple metabolic reaction. This method is combined with a genetic algorithm, which already obtains sets of correct parameters, but it is not yet able to choose the best one between them, due to the models nature. The set of parameters is slightly different between several runs of the genetic algorithm although the value of their fitness function results to be the same. This is why it is necessary an additional method which complements the genetic algorithm achieving a unique optimum.With the proposal here formulated, a criteria has been defined to analyse the results given by the genetic algorithm. Such criteria is based in the minimization of an energy function which is the main characteristic of the global optimization method called simulated annealing. This function makes possible to observe how the biological model evolves towards stability according to the values of minimum energy and the different sets of parameters resulting from the genetic algorithm. The results presented in this document show the effectiveness of the method proposed

    A Comprehensive Study Of Esterification Of Free Fatty Acid To Biodiesel In a Simulated Moving Bed System

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    Simulated Moving Bed (SMB) systems are used for separations that are difficult using traditional separation techniques. Due to the advantage of adsorption-based chromatographic separation, SMB has shown promising application in petrochemical and sugar industries, and of late, for chiral drug separations. In recent years, the concept of integration of reaction and in-situ separation in a single unit has achieved considerable attention. The simulated moving bed reactor (SMBR) couples both these unit operations bringing down the operation costs while improving the process performance, particularly for products that require mild operating conditions. However, its application has been limited due to complexity of the SMBR process. Hence, to successfully implement a reaction in SMB, a detailed understanding of the design and operating conditions of the SMBR corresponding to that particular reaction process is necessary. Biodiesel has emerged has a viable alternative to petroleum-based diesel as a renewable energy source in recent years. Biodiesel can be produced by esterification of free fatty acids (present in large amounts in waste oil) with alcohol. The reaction is equilibrium-limited, and hence, to achieve high purity, additional purification steps increases the production cost. Therefore, combining reaction and separation in SMBR to produce high purity biodiesel is quite promising in terms of bringing down the production cost. In this work, the reversible esterification reaction of oleic acid with methanol catalyzed by Amberlyst 15 resin to form methyl oleate (biodiesel) in SMBR has been investigated both theoretically and experimentally. First, the adsorption and kinetic constants were determined for the biodiesel synthesis reaction by performing experiments in a single column packed with Amberlyst 15, which acts as both adsorbent and catalyst. Thereafter, a rigorous model was used to describe the dynamic behaviour of multi-column SMBR followed by experimental verification of the mathematical model. Sensitivity analysis is done to determine robustness of the model. Finally, a few simple multi-objective optimization problems were solved that included both existing and design-stage SMBRs using non-dominated sorting genetic algorithm (NSGA). Pareto-optimal solutions were obtained in both cases, and moreover, it was found that the performance of the SMBR could be improved significantly under optimal operating conditions

    Mathematical modelling of myltiphase reaction of renewable and mineral diesel fuels

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    Višefazni reakcioni procesi predstavljaju ključne stupnjeve u proizvodnji dizel goriva, obnovljivih i mineralnih. U savremenim rafinerijama proces hidrotritinga zauzima značajno mesto pri čemu se odvija u prisustvu čvrstog katalizatora. Glavni ciljevi procesa hidrotritinga u preradi nafte su uklanjanje sumpora, stabilizacija proizvoda i uklanjanje drugih neželjenih primesa. Hidrodesulfurizacija gasnog ulja se odvija u reaktoru sa nepokretnim slojem katalizatora uz prisustvo vodonika na povišenom pritisku (uobičajeno do 60 bar-a) i temperaturi (633 K). U ovoj disertaciji razvijen je deterministički matematički model reaktora za simulaciju procesa. Model čine diferencijalni bilansi toplote i hemijskih vrsta, kao i odgovarajuće kinetičke jednačine za reakcije desulfurizacije. Sistem diferencijalnih jednačina koje čine model je rešavan primenom programa napisanog u MATLAB-u. Višefazni reakcioni proces sinteze biodizela je modelovan i simuliran za nekatalitičku etanolizu i metanolizu triglicerida na povišenom pritisku i temperaturi. Raspodela faza i ravnoteža za sistem para – tečnost i para – tečnost – tečnost metanola i etanola sa trioleinom je ispitivana sa ciljem određivanja parametara koji omogućavaju visoku konverziju ulja. Simlacija ravnoteže faza korišćenjem RK-Aspen jednačine stanja u UniSim softveru pokazala je veoma dobro slaganje sa eksperimentalnim podacima. Rezultati simulacija ukazuju na važan uticaj na reakcioni mehanizam i na ukupnu kinetiku procesa u sub-kritičnoj oblasti (T < 270 ◦C na 200 bar-a) s obzirom da u početnoj fazi reakcije egzistiraju dve faze, što uzrokuje lošiji kontakt između reaktanata. U slučaju postojanja jedne reakcione faze (T > 270 ◦C na 200 bar-a) inicijalno visoka brzina reakcije je limitirana na visokim konverzijama zbog porasta stepena odigravanja povratne reakcije. Matematički model nekatalitičke sinteze biodizela je tretiran kao kompleksna uzastopno – paralelna povratna reakcija. Kinetički parametri su određeni primenom standardnih optimizacionih metoda i najbolji rezultati su dobijeni primenom metode genetičkog algoritma. Primena ove metode za određivanje kinetičkih parametara rezultovala je povešanom preciznošću predviđanja koncentracija važnih intermedijera, monoglicerida i diglicerida. Energije aktivacije u izrazima za kinetičke konstante dobijene primenom metode genetičkog algoritma su u veoma dobrom slaganju sa teoretskim vrednostima određenim metodom proračuna molekulskih orbitala.Multiphase reaction processes constitute key steps in manufacturing of diesel fuels, both renewable and mineral. In modern refineries there is a major role for the hydrotreating processes that operate under high pressure in the presence of a solid catalyst. The main role of hydrotreating within the petroleum refining is the removal of sulphur compounds, the stabilization of the product and the removal of other undesirable impurities. Hydrodesulphurization of gas oil takes place in a reactor with a fixed bed catalyst in the presence of hydrogen at elevated pressure (normally up to 60 bar) and temperature (633 K). In this thesis, a mathematical model of a deterministic type was developed and used to simulate the hydrotreating reactor operation. The model consists of differential balance equations of heat and chemical species, and the corresponding kinetic equations for the reactions of hydrodesulphurization of sulphur compounds. The system of differential equations that constitute the mathematical model was solved using the MATLAB software package. Multiphase reactions for biodiesel synthesis were modeled and simulated for non-catalytic methanolysis and ethanolysis of triglycerides under high pressure and at elevated temperature. The vapour–liquid or vapour–liquid–liquid equilibrium and phase distribution of methanol and ethanol with triolein were investigated in order to determine the range of pressure and temperature required for high oil conversion. Simulation of phase equilibrium using RK-Aspen EOS and UniSim software were found to correlate well with the experimental data. Simulation results show the important influence of the phase equilibrium on the reaction mechanism and overall kinetics under subcritical conditions (T < 270 ◦C at 200 bar) since the two liquid phases exist at the beginning of reaction, thereby limiting the contact between the reactants. In case of single reaction phase (T > 270 ◦C at 200 bar) the initially high reaction rate is limited at high conversion levels due to increasing extent of reversible reaction. Mathematical model of non-catalytic biodiesel synthesis was treated as complex parallel and consequtive reversible reaction. Kinetic parameters were estimated using standard optimization methods and the best results were obtained with Genetic Algorithm procedure. The application of this method resulted in kinetic parameters with improved accuracy in predicting concentrations of important reaction intermediates, i.e. diglycerides and monoglycerides. Activation energies of kinetic parameters obtained by the Genetic Algorithm method are in very agreement with theoretical values determined by molecular orbital calculations

    Metal Nanoparticles as Catalysts for Green Applications

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    This reprint of “Metal Nanoparticles as Catalysts for Green Applications” collects recent works of researchers on metal nanoparticles as catalysts for green applications. All works deal with designing chemical products and processes that generate and use less (or preferably no) hazardous substances by applying the principles of green chemistry. Despite the interdisciplinary nature of the different applications involved, ranging from pure chemistry to material science, from chemical engineering to physical chemistry, in this reprint there are common characteristics connecting the areas together, and they can be described by two words: sustainability and catalysis

    계산 과학적 접근을 통한 지속가능한 공정의 최적 설계 및 산업에의 응용

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    학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부, 2018. 2. 이원보.Recently, in the field of chemical engineering, many types of research based on high-performance computing have been combined with computer-aided process systems engineering. Therefore, various techniques of computational science such as computational fluid dynamics, optimization methodology, and machine learning have been applied to the problems of chemical reactor modeling and process optimization. Notably, in this advance computational science approach, the scope of research extends to non-traditional fields such as reactive research according to the 3D shape of the reactor that has not been easily solved in the past and surrogate model based optimization using machine learning. In this thesis, various methods are proposed to obtain the maximum profit with minimum cost by making a breakthrough design. In parallel, there is a growing demand for sustainable chemical processes in chemical engineering. Conventional chemical processes are highly dependent on oil prices, and unless a diverse portfolio is designed, the sustainability of their chemical industries can be violated because of the oil controlling from the Middle East or US. In addition, these crude oil based chemical processes and power plants generate a great deal of CO2. Therefore, it is not necessary to capture these CO2 and make only meaningless storage but to reproduce it as a product that can be used and make it economical carbon capture, utilization, and storage (CCUS) technology. To solve this series of processes, the Gas-to-Liquid (GTL) process and CCUS are being researched and developed in various ways. In this thesis, I will discuss the process modeling, optimizing, and designing the reactor and process using CFD, mathematical programming, machine learning, deep learning, and derivative-free optimization techniques in computational science. First of all, the Fischer-Tropsch microchannel reactor and 3-phase carbonation reactor, which are the key reactor of two most important processes of the sustainable process, the gas-to-liquid process (GTL) and the carbon capture, utilization, and storage (CCUS), are modeled by CFD. Also, we propose an integration platform of CFD model and process simulator and conduct research from the point of view of combining with existing process engineering. With these advanced reactor model, we propose a multi-objective optimization methodology using a stochastic optimization algorithm, a genetic algorithm (GA) with e-constraint method for simultaneously maximizing C5+ productivity and minimizing the temperature rise of a Fischer-Tropsch microchannel reactor. The main mixed integer nonlinear programming (MINLP) optimization problem is decomposed into an external CFD reactor model function and internal optimization constraints. The methodology is applied to the catalyst packing zone division, which is divided and packed with a different dilution ratio to distribute the heat of reaction evenly. The best solutions of the proposed optimizer are reproducible with different crossover fractions and are more efficient than other traditional non-convex constraint local solvers. Based on the Pareto optimal solution of the final optimizer with 4 zones, discrete dilution increases C5+ productivity to 22% and decreases ∆Tmax to 63.2% compared to the single zone catalyst packing case. Finally, several Pareto optimal solutions and sub-optimal solutions are compared and the results are documented in terms of C5+ productivity and maximum temperature increase. In process scale optimization platform, a modified DIRECT algorithm with a sub-dividing step for considering hidden constraints is proposed. The effectiveness of the algorithm is exemplified by its application to a cryogenic mixed refrigerant process using a single mixed refrigerant for natural gas liquefaction and its comparison with a well-known stochastic algorithm (GA, PSO, SA), and model based search algorithm (SNOBFIT), local solver (GPS, GSS, MADS, active-set, interior-point, SQP), and other hidden constraint handling methods, including the barrier approach and the neighborhood assignment strategy. Optimal solution calculated by the proposed algorithms decreases the specific power required for natural gas liquefaction to 18.9% compared to the base case. In the same chapter, heat exchanger network synthesis (HENS) has progressed by using mathematical programming-based simultaneous methodology. Although various considerations such as non-isothermal mixing and bypass streams are applied to consider real world alternatives in modeling phase, many challenges are faced because of its properties within non-convex mixed-integer nonlinear programming (MINLP). We propose a modified superstructure, which contains a utility substage for use in considering multiple utilities in a simultaneous MINLP model. To improve model size and convergence, fixed utility locations according to temperature and series connections between utilities are suggested. The numbers of constraints, discrete, and continuous variables show that overall model size decreases compared with previous research. Thus, it is possible to expand the feasible search area for reaching the nearest global solution. The models effectiveness and applications are exemplified by several literature problems, where it is used to deduce a network superior to that of any other reported methodology. In the case of plant-wide scale systems, a non-linear surrogate model based on deep learning is proposed using a variational autoencoder with deep convolutional layers and a deep neural network with batch normalization (VAEDC-DNN) for real-time analysis of the probability of death (Pdeath). VAEDC can extract representation features of the Pdeath contour with complicated urban geometry in the latent space, and DNN maps the variable space into the latent space for the Pdeath image data. The chlorine gas leak accident in the Mipo complex (city of Ulsan, Republic of Korea) is used for verification of the model. The proposed model predicts the Pdeath image within a mean squared error of 0.00246, and compared with other models, it exhibits superior performance. Furthermore, through the smoothness of image transition in the variable space, it is confirmed that image generation is not overfitting by data memorization. Finally, a pilot scale (1.0 BPD) compact GTL process comprising of reforming section, CO2 separating section and Fischer -Tropsch (FT) synthesis section is presented. Systematic design procedure adopted for the design of a modular 0.5 BPD microchannel FT reactor block design consisting of 528 process channels is described. On average 98.27% CH4 conversion to syngas in reforming section comprising of a pre-reformer unit and a tri-reformer unit, CO2 separation rate of 36.75 % along with CO/H2 reduction from 2.67 to 2.08 in CO2 membrane separation section comprising of three membrane separators, for the entire plant operation duration of 450 hr demonstrated successful and stable operation of pre-processing sections of the present pilot-scale compact GTL process. Parallel operation of FT microchannel reactor and multitubular fixed bed type FT reactor proved failure for latter due to reaction runaway, while the former showed stable operation with high CO conversion of 83% and successful temperature control (at 220 oC, 230 oC and at 240 oC during the 139 hr operation), which demonstrated the appreciable performance of KOGAS-SNU novel microchannel FT reactor. Furthermore, a tank agitator carbonation reactor in which the reaction between calcium oxide and carbon dioxide takes place is studied to understanding that how 6 design variables (the number of impeller, impeller type, D/T, clearance, speed, baffle) affect to the solid dispersion using CFD simulation.CHAPTER 1. Introduction 17 1.1. Research motivation 17 1.1.1. Chronological stages of development of process design 19 1.1.2. Current status of process systems engineering with computational science approach 21 1.1.3. Introduction to the sustainable process 23 1.2. Research objectives 25 1.3. Outline of the thesis 26 1.4. Associated publications 29 CHAPTER 2. Study of the Novel Reactor Models using Computational Science 30 2.1. Introduction 30 2.2. Gas-to-Liquid (GTL) Fischer-Tropsch (FT) reactor model 32 2.2.1. 2D axisymmetric computational fluid dynamics (CFD) based Fischer-Tropsch microchannel reactor single-channel model 37 2.2.2. 3D CFD based Fischer-Tropsch microchannel reactor multi-channel model 52 2.3. Carbon Capture, Utilization, and Storage (CCUS) multiphase carbonation reactor model 75 2.3.1. Rigorous reaction kinetics for carbonation based CCUS reactor 77 2.3.2. Eulerian multiphase model for carbonation reactor 92 2.4. CFD-Process integrated platform for simultaneous process and reactor design 105 2.4.1. Introduction 105 2.4.2. Model formulation 106 2.4.3. Result and discussion 112 2.4.4. Conclusion 116 CHAPTER 3. Optimization for the Unit, Process, and Plant-wide Systems 117 3.1. Introduction 117 3.2. Reactor systems scale optimization 119 3.2.1. Multi-objective optimization of microchannel reactor for Fischer-Tropsch synthesis using computational fluid dynamics and genetic algorithm 119 3.3. Process systems scale optimization 152 3.3.1. A modified DIRECT algorithm for hidden constraints optimization problem 152 3.3.2. Simultaneous synthesis of a heat exchanger network with multiple utilities using utility substages 200 3.4. Plant-wide systems scale modeling and optimization 233 3.4.1. Toxic gas release modeling for real-time analysis using variational autoencoder with convolution neural networks 233 CHAPTER 4. Industrial Applications 276 4.1. Optimal Design and Operation of Fischer-Tropsch Microchannel Reactor for Pilot Scale Compact Gas-to-Liquid Process 276 4.1.1. Pilot scale compact GTL process 277 4.1.2. Microchannel FT reactor design 286 4.1.3. Pilot plant experiment 287 4.1.4. Result and discussion 291 4.1.5. Conclusion 304 4.2. Industrial scale (40 tonCO2/day) CCUS carbonation reactor geometry design optimization 306 4.2.1. Design procedure and simulation set-up 310 4.2.2. Result and Discussion 313 4.2.3. Conclusion 332 CHAPTER 5. Concluding Remarks 334 5.1. Summary of Contributions 334 5.2. Future Work 337 Nomenclature 340 Reference 346 Abstract in Korean (국문초록) 360Docto
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