91 research outputs found

    Plantwide Control and Simulation of Sulfur-Iodine Thermochemical Cycle Process for Hydrogen Production

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    A PWC structure has developed for an industrial scale SITC plant. Based on the performance evaluation, it has been shown that the SITC plant developed via the proposed modified SOC structure can produce satisfactory performance – smooth and reliable operation. The SITC plant is capable of achieving a thermal efficiency of 69%, which is the highest attainable value so far. It is worth noting that the proposed SITC design is viable on the grounds of economic and controllability

    Optimal Design and Operation of Heat Exchanger Network

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    Heat exchanger networks (HENs) are the backbone of heat integration due to their ability in energy and environmental managements. This thesis deals with two issues on HENs. The first concerns with designing of economically optimal Heat exchanger network (HEN) whereas the second focus on optimal operation of HEN in the presence of uncertainties and disturbances within the network. In the first issue, a pinch technology based optimal HEN design is firstly implemented on a 3–streams heat recovery case study to design a simple HEN and then, a more complex HEN is designed for a coal-fired power plant retrofitted with CO2 capture unit to achieve the objectives of minimising energy penalty on the power plant due to its integration with the CO2 capture plant. The benchmark in this case study is a stream data from (Khalilpour and Abbas, 2011). Improvement to their work includes: (1) the use of economic data to evaluate achievable trade-offs between energy, capital and utility cost for determination of minimum temperature difference; (2) redesigning of the HEN based on the new minimum temperature difference and (3) its comparison with the base case design. The results shows that the energy burden imposed on the power plant with CO2 capture is significantly reduced through HEN leading to utility cost saving maximisation. The cost of addition of HEN is recoverable within a short payback period of about 2.8 years. In the second issue, optimal HEN operation considering range of uncertainties and disturbances in flowrates and inlet stream temperatures while minimizing utility consumption at constant target temperatures based on self-optimizing control (SOC) strategy. The new SOC method developed in this thesis is a data-driven SOC method which uses process data collected overtime during plant operation to select control variables (CVs). This is in contrast to the existing SOC strategies in which the CV selection requires process model to be linearized for nonlinear processes which leads to unaccounted losses due to linearization errors. The new approach selects CVs in which the necessary condition of optimality (NCO) is directly approximated by the CV through a single regression step. This work was inspired by Ye et al., (2013) regression based globally optimal CV selection with no model linearization and Ye et al., (2012) two steps regression based data-driven CV selection but with poor optimal results due to regression errors in the two steps procedures. The advantage of this work is that it doesn’t require evaluation of derivatives hence CVs can be evaluated even with commercial simulators such as HYSYS and UNISIM from among others. The effectiveness of the proposed method is again applied to the 3-streams HEN case study and also the HEN for coal-fired power plant with CO2 capture unit. The case studies show that the proposed methodology provides better optimal operation under uncertainties when compared to the existing model-based SOC techniques

    Operator Training Simulator Using Plantwide Control for Biodiesel Production from Waste Cooking Oil

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    Kajian ini bertujuan untuk membangunkan simulator latihan operator (OTS) untuk mangkin homogen bagi proses dua langkah biodiesel yang kompleks. Latihan sambil bekerja selalunya memerlukan kos yang tinggi, berisiko dan tidak lengkap kerana beberapa situasi kecemasan mungkin tidak berlaku semasa sesi latihan. Biodiesel dilihat sebagai sumber bahan api alternative, Disebabkan ketersediaan yang terhad sumber tenaga yang tidak boleh diperbaharui dan juga kebimbangan terhadap alam sekitar. Walau bagaimanapun, kos pengeluaran yang tinggi bagi biodiesel menghadkan pengeluaran dan penggunaannya. Salah satu pilihan yang terbaik adalah dengan menggunakan sisa minyak masak (WCO) sebagai sumber bahan mentah bagi pengeluaran biodiesel yang kos efektif dan juga penggunaan WCO yang berkesan. Dalam kajian ini, sisa minyak sawit masak dianggap dengan 6% asid lemak bebas (FFA) sebagai bekalan simpanan. Dua proses pengeluaran biodiesel (kedua-duanya melibatkan pengesteran asid dan transesterifikasi alkali) telah dibandingkan untuk analisis ekonomi dan alam sekitar. Pertama, proses ini dalam simulator Aspen Plus. Selepas itu, kedua-dua proses dioptimumkan dengan mengambil kira keuntungan, tenaga haba dan bahan buangan organik sebagai objektif, dan menggunakan program berasaskan Excel pengoptimuman multi-objektif (EMOO) untuk pengisihan algoritma genetic elitis tidak dikuasai (NSGA-II). Proses 1 mempunyai tiga reaktor transesterifikasi yang menghasilkan sisa organik jauh lebih rendah (32%), memerlukan duti haba yang lebih rendah (39%) dan sedikit keuntungan (1.6%) berbanding Proses 2 yang hanya mempunyai satu reaktor transesterifikasi dan juga urutan pemisahan yang berbeza. Sistem kawalan loji lebar (PWC) yang berkesan adalah penting untuk operasi loji biodiesel yang selamat, lancar dan ekonomi. Oleh itu, sistem PWC yang sesuai telah dibangunkan untuk proses biodiesel yang menggunakan simulasi rangka kerja bersepadu dan heuristik (IFSH). Merit utama metodologi IFSH adalah keberkesanan penggunaan proses simulator yang baik dan heuristik dalam membangunkan sistem PWC dan kesederhanaan applikasinya. Akhir sekali, pelaksanaan sistem kawalan yang dibangunkan dinilai dari segi masa penetapan, sisihan daripada sasaran pengeluaran (DPT), dan jumlah variasi keseluruhan (TV) dalam pembolehubah yang dimanipulasi. Penilaian-penilaian prestasi dan keputusan simulasi dinamik menunjukkan bahawa sistem PWC yang dihasilkan adalah stabil, berkesan, dan teguh terhadap beberapa gangguan. Akhir sekali, OTS telah dibangunkan untuk penghasilan biodiesel daripada WCO. Oleh itu, latihan menggunakan OTS adalah penting. OTS telah dibangunkan untuk pengeluaran biodiesel dan telah diapplikasikan dengan beberapa keadaan proses yang tidak normal. Keadaan proses ini boleh dimuatkan dan digunakan pada bila-bila masa untuk melatih operator baru dan sedia ada. Kajian ini adalah yang pertama dibangunkan menggunakan struktur lengkap PWC dan OTS untuk mangkin yang homogeneous bagi dua langkah pengeluaran biodiesel daripada WCO. ________________________________________________________________________________________________________________________ This study aims at developing an operator training simulator (OTS) for the complex homogeneously catalyzed two-step biodiesel process. On-job training is often costly, risky and incomplete as some emergency situations may not arise during the training session. Therefore, training using an OTS is crucial. Pertaining to the limited availability of non-renewable energy sources and the environmental concerns, biodiesel is considered as a potential alternative fuel. However, the high production cost of biodiesel limits its manufacture and utilization. One attractive option is to use waste cooking oil (WCO) as the feedstock that enables cost effective biodiesel production and also facilitates effective WCO utilization. This study considers waste cooking palm oil with 6% free fatty acids (FFA) as feedstock. Two biodiesel production processes (both involving acid esterification and alkali transesterification) are compared for economic and environmental objectives. Firstly, these processes are simulated realistically in Aspen Plus simulator. Subsequently, both the processes are optimized considering profit, heat duty and organic waste as objectives, and using an Excel-based multi-objective optimization (EMOO) program for the elitist non-dominated sorting genetic algorithm (NSGA-II). Process 1 having three transesterification reactors produces significantly lower organic waste (by 32%), requires lower heat duty (by 39%) and slightly more profitable (by 1.6%) compared to Process 2 having a single transesterification reactor and also a different separation sequence. An effective plantwide control (PWC) system is crucial for the safe, smooth, and economical operation of a biodiesel plant. Hence, a suitable PWC system is developed for the biodiesel process using the integrated framework of simulation and heuristics (IFSH). The main merits of the IFSH methodology are effective use of rigorous process simulators and heuristics in developing a PWC system and simplicity of application. Later, the performance of the developed control system is assessed in terms of settling time, deviation from the production target (DPT), and overall total variation (TV) in manipulated variables. These performance assessments and the results of dynamic simulations showed that the developed PWC system is stable, effective, and robust in the presence of several disturbances. Finally, an OTS has been developed for the biodiesel production from WCO. The developed OTS for biodiesel production process has been investigated for several abnormal process conditions. These process scenarios can be loaded and utilized at any point in time to train the new and existing operators. This is the first study to develop a complete PWC structure and OTS for a homogeneously catalyzed two-step biodiesel production from WCO

    Assessing plant design with regards to MPC performance using a novel multi-model prediction method

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    Model Predictive Control (MPC) is nowadays ubiquitous in the chemical industry and offers significant advantages over standard feedback controllers. Notwithstanding, projects of new plants are still being carried out without assessing how key design decisions, e.g., selection of production route, plant layout and equipment, will affect future MPC performance. The problem addressed in this Thesis is comparing the economic benefits available for different flowsheets through the use of MPC, and thus determining if certain design choices favour or hinder expected profitability. The Economic MPC Optimisation (EMOP) index is presented to measure how disturbances and restrictions affect the MPC’s ability to deliver better control and optimisation. To the author’s knowledge, the EMOP index is the first integrated design and control methodology to address the problem of zone constrained MPC with economic optimisation capabilities (today's standard in the chemical industry). This approach assumes the availability of a set of linear state-space models valid within the desired control zone, which is defined by the upper and lower bounds of each controlled and manipulated variable. Process economics provides the basis for the analysis. The index needs to be minimised in order to find the most profitable steady state within the zone constraints towards which the MPC is expected to direct the process. An analysis of the effects of disturbances on the index illustrates how they may reduce profitability by restricting the ability of an MPC to reach dynamic equilibrium near process constraints, which in turn increases product quality giveaway and costs. Hence the index monetises the required control effort. Since linear models were used to predict the dynamic behaviour of chemical processes, which often exhibit significant nonlinearity, this Thesis also includes a new multi-model prediction method. This new method, called Simultaneous Multi-Linear Prediction (SMLP), presents a more accurate output prediction than the use of single linear models, keeping at the same time much of their numerical advantages and their relative ease of obtainment. Comparing the SMLP to existing multi-model approaches, the main novelty is that it is built by defining and updating multiple states simultaneously, thus eliminating the need for partitioning the state-input space into regions and associating with each region a different state update equation. Each state’s contribution to the overall output is obtained according to the relative distance between their identification point, i.e., the set of operating conditions at which an approximation of the nonlinear model is obtained, and the current operating point, in addition to a set of parameters obtained through regression analysis. Additionally, the SMLP is built upon data obtained from step response models that can be obtained by commercial, black-box dynamic simulators. These state-of-the-art simulators are the industry’s standard for designing large-scale plants, the focus of this Thesis. Building an SMLP system yields an approximation of the nonlinear model, whose full set of equations is not of the user’s knowledge. The resulting system can be used for predictive control schemes or integrated process design and control. Applying the SMLP to optimisation problems with linear restrictions results in convex problems that are easy to solve. The issue of model uncertainty was also addressed for the EMOP index and SMLP systems. Due to the impact of uncertainty, the index may be defined as a numeric interval instead of a single number, within which the true value lies. A case of study consisting of four alternative designs for a realistically sized crude oil atmospheric distillation plant is provided in order to demonstrate the joint use and applicability of both the EMOP index and the SMLP. In addition, a comparison between the EMOP index and a competing methodology is presented that is based on a case study consisting of the activated sludge process of a wastewater treatment plant

    Energy efficient control and optimisation techniques for distillation processes

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    PhD ThesisDistillation unit is one of the most energy intensive processes and is among the major CO2 emitter in the chemical and petrochemical industries. In the quest to reduce the energy consumption and hence the environmental implications of unutilised energy, there is a strong motivation for energy saving procedures for conventional columns. Several attempts have been made to redesign and heat integrate distillation column with the aim of reducing the energy consumption of the column. Most of these attempts often involve additional capital costs in implementing. Also a number of works on applying the second law of thermodynamics to distillation column are focused on quantifying the efficiency of the column. This research aims at developing techniques of increasing the energy efficiency of the distillation column with the application of second law using the tools of advanced control and optimisation. Rigorous model from the fundamental equations and data driven models using Artificial neural network (ANN) and numerical methods (PLS, PCR, MLR) of a number of distillation columns are developed. The data for the data driven models are generated from HYSYS simulation. This research presents techniques for selecting energy efficient control structure for distillation processes. Relative gain array (RGA) and relative exergy array (REA ) were used in the selection of appropriate distillation control structures. The viability of the selected control scheme in the steady state is further validated by the dynamic simulation in responses to various process disturbances and operating condition changes. The technique is demonstrated on two binary distillation systems. In addition, presented in this thesis is optimisation procedures based on second law analysis aimed at minimising the inefficiencies of the columns without compromising the qualities of the products. ANN and Bootstrap aggregated neural network (BANN) models of exergy efficiency were developed. BANN enhances model prediction accuracy and also provides model prediction confidence bounds. The objective of the optimisation is to maximise the exergy efficiency of the column. To improve the reliability of the optimisation strategy, a modified objective function incorporating model prediction confidence bounds was presented. Multiobjective optimisation was also explored. Product quality constraints introduce a measure of penalization on the optimisation result to give as close as possible to what obtains in reality. The optimisation strategies developed were applied to binary systems, multicomponents system, and crude distillation system. The crude distillation system was fully explored with emphasis on the preflash unit, atmospheric distillation system (ADU) and vacuum distillation system (VDU). This study shows that BANN models result in greater model accuracy and more robust models. The proposed ii techniques also significantly improve the second law efficiency of the system with an additional economic advantage. The method can aid in the operation and design of energy efficient column.Commonwealth scholarship commissio

    Self-optimizing control – A survey

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    Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by the fact that an economic objective function is adopted as a selection criterion. The aim is to systematically select the controlled variables such that by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. If a selection leads to an acceptable economic loss compared to perfectly optimal operation then the chosen control structure is referred to as “self-optimizing”. In this comprehensive survey on methods for finding self-optimizing controlled variables we summarize the progress made during the last fifteen years. In particular, we present brute-force methods, local methods based on linearization, data and regression based methods, and methods for finding nonlinear controlled variables for polynomial systems. We also discuss important related topics such as handling changing active constraints. Finally, we point out open problems and directions for future research

    Framework for operability assessment of production facilities: an application to a primary unit of a crude oil refinery

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    This work focuses on the development of a methodology for the optimization, control and operability of both existing and new production facilities through an integrated environment of different technologies like process simulation, optimization and control systems. Such an integrated environment not only creates opportunities for op¬erational decision making but also serves as training tool for the novice engineers. It enables them to apply engineering expertise to solve challenges unique to the process industries in a safe and virtual environment and also assist them to get familiarize with the existing control systems and to understand the fundamentals of the plant operation. The model-based methodology proposed in this work, starts with the implementation of first principle models for the process units on consideration. The process model is the core of the methodology. The state of art simulation technologies have been used to model the plant for both steady state and dynamic state conditions. The models are validated against the plant operating data to evaluate the reliability of the models. Then it is followed by rigorously posing a multi-optimization problem. In addition to the basic economic variables such as raw materials and operating costs, the so-called “triple-bottom-line” variables related with sustainable and environmental costs are incorporated into the objective function. The methodologies of Life Cycle Assessment (LCA) and Environmental Damage Assessment (EDA) are applied within the optimization problem. Subsequently the controllability of the plant for the optimum state of conditions is evaluated using the dynamic state simulations. Advanced supervisory control strategies like the Model Predictive Control (MPC) are also implemented above the basic regulatory control. Finally, the methodology is extended further to develop training simulator by integrating the simulation case study to the existing Distributed Control System (DCS). To demonstrate the effectiveness of the proposed methodology, an industrial case study of the primary unit of the crude oil refinery and a laboratory scale packed distillation unit is thoroughly investigated. The presented methodology is a promising approach for the operability study and optimization of production facilities and can be extended further for an intelligent and fully-supportable decision making

    Operation and Design of Diabatic Distillation Processes

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