Effect of Delta Temperature Minimum Contribution in Obtaining an Operable and Flexible Heat Exchanger Network

This paper presents the control structure decision making for heat exchanger networks (HENs) to obtain operable and flexible network. Delta temperature minimum (ΔTmin) contribution is considered in this study. Several studies have been done to determine the effect of ΔTmin-contribution on the annual cost. Usually, HENs designed without consider controllability analysis and control structure decision making. In control structure decision making analysis are done to already designed HEN. Design and controllability analysis for HEN are done seperately. Therefore, there are still lacks of studies on how the ΔTmin-contribution effects the controllability and control structure desion making. Optimal ΔTmin selection is important decision to make in the early stage to avoid inflexible and inoperable heat exchanger networks. The question that needs to be answerd here is how to determine the optimal value of ΔTmin that will have better operating conditions that satisfy process design (HEN), controllability and as well as economy. In this study, this problem will be formulated as a mathematical programming (optimizattion with constraints) and solved by decomposing it into four hierarchiacal stages: (i) target selection, (ii) HEN design analysis, (iii) controllability analysis, and (iv) optimal selection and verification. A case study plant was selected as a case study. Small value of ΔTmin was first implemented and will gradually be increased to see the effect on the operability and flexibility of a case study

An Improved Method for Predicting Heat Exchanger Network Area

Successful application of pinch analysis to any process, be it for grassroots design or retrofit, depends upon the extent to which set targets are achieved in practice. This entails predicating the three stages of process integration namely targeting, synthesis and detailed design on the same basis. There exist gap between these three stages largely due to inaccuracies in film heat transfer coefficient and inability to replicate same at the various stages. This paper presents an improved methodology for area targeting that is consistent with detailed design of an exchanger not just because it is premised on the same basis of pressure drop constraints but, more importantly, because it allows, for necessary variation of stream properties with temperature. The validity of the methodology has been tested using two case studies from the literature. The results obtained in all studies reveal a difference of less than 2% between targeting, synthesis and detailed design with the new methodology. This is contrary to the difference of as high as 59% between targeting and detailed design obtained with the state-of-the-art methodology. There is therefore an excellent agreement between the three stages of process integration arising from the new methodology. Keywords: heat exchanger network, area targeting, synthesis, detailed design, pressure drop, film heat transfer coefficient

Safety and CO2 emissions: Implications of using organic fluids in a ship’s waste heat recovery system

Current Marine Policies and regulations greatly favour the use of efficiency enhancing technologies such as the Organic Rankine Cycle (ORC) waste heat recovery systems (WHRS), through the entry into force of International Maritime Organisation (IMO) Energy Efficiency Design Index (EEDI). However, safety regulations such as IMO Safety Of Life At Sea (SOLAS), International Gas Code and Classification Societies still consider the use of highly flammable organic fluids on board ships as hazardous and undesirable, requiring special Administration approval. The benefits of organic fluids in emerging technologies will likely increase their usefulness on board in the near future. Furthermore, current ship safety systems and integrated platform management systems greatly reduce the risks associated with their low flash point making them acceptable for marine use given specific design considerations. This paper studies the case of an Aframax tanker navigating the route North Sea – Naantali, Finland using a slow speed diesel engine. A code with a multi-objective optimization approach generated explicitly for this purpose produces different optimal WHRS designs for the vessel’s operating profile. The WHRS is installed after the turbo compressors in the exhaust gas system, where it absorbs part of the available waste heat and converts it to electricity using a generator. This results in a reduction in fuel consumption, hence decreasing the emission of greenhouse gases. The different optimal designs are compared with a steam WHRS to show the strengths and weaknesses of using an ORC WHRS on board. The ORC technology is at its early stages of development in the marine field, it is important that safety policies follow the evolution of the technology and its associated safety equipment. This paper will serve to recognize the specific safety considerations associated with the ORC and highlight the advantages of carrying organic fluids on board as a solution to increasing CO2 emission restrictions and other environmental concerns

Making shipping greener: A vessel’s waste heat recovery system comparative study between organic fluids and water

The largest source of energy loss in a ship is found in the propulsion system. This study focuses on the concept of managing waste heat energy from the exhaust gas. Using waste heat recovery systems to make shipping more efficient represents a good area of opportunity for achieving the shipping industry’s green objectives. Organic Rankine Cycles (ORC) have been applied in land based systems before, showing improvements in performance when compared with the traditional Rankine cycle (RC). ORC plants on board ships face different challenges such as variable operating conditions and limited space. As marine environmental rules require greener vessels and engine thermal efficiency continue to increase, ORC waste heat recovery systems become a more attractive option. The proposed waste heat recovery system (WHRS) was modelled using Matlab with a typical ship installation with a slow speed diesel engine and the WHRS installed after the turbo compressors in the exhaust gas system. The energy recovered from the exhaust gas flow is transformed via the thermodynamic cycle into electricity which will help to cover the ship’s demand. The Matlab code found the highest electric power output varying the WHRS high pressure, maximizing the fuel and CO2 emission reductions. Water and various organic fluids were considered as working fluids and their performance compared over a range of different engine operating scenarios in order to assess the differences between a marine ORC and RC. A representative ship operating profile and a typical marine generator were used to measure CO2 emission reductions and the implications of having flammable organic fluids on-board. This work demonstrates that a simple ORC can be more effective than water based RC for the same engine operating condition

계산 과학적 접근을 통한 지속가능한 공정의 최적 설계 및 산업에의 응용

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