Browsing by Author "Kokossis, Antonis"
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Item Open Access Optimal Design and Operation of Heat Exchanger Network(Cranfield University, 2015-08) Salihu, Adamu Girei; Cao, Yi; Kokossis, AntonisHeat 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.Item Open Access Petroleum refinery scheduling with consideration for uncertainty(Cranfield University, 2015-07) Hamisu, Aminu Alhaji; Cao, Yi; Kokossis, AntonisScheduling refinery operation promises a big cut in logistics cost, maximizes efficiency, organizes allocation of material and resources, and ensures that production meets targets set by planning team. Obtaining accurate and reliable schedules for execution in refinery plants under different scenarios has been a serious challenge. This research was undertaken with the aim to develop robust methodologies and solution procedures to address refinery scheduling problems with uncertainties in process parameters. The research goal was achieved by first developing a methodology for short-term crude oil unloading and transfer, as an extension to a scheduling model reported by Lee et al. (1996). The extended model considers real life technical issues not captured in the original model and has shown to be more reliable through case studies. Uncertainties due to disruptive events and low inventory at the end of scheduling horizon were addressed. With the extended model, crude oil scheduling problem was formulated under receding horizon control framework to address demand uncertainty. This work proposed a strategy called fixed end horizon whose efficiency in terms of performance was investigated and found out to be better in comparison with an existing approach. In the main refinery production area, a novel scheduling model was developed. A large scale refinery problem was used as a case study to test the model with scheduling horizon discretized into a number of time periods of variable length. An equivalent formulation with equal interval lengths was also presented and compared with the variable length formulation. The results obtained clearly show the advantage of using variable timing. A methodology under self-optimizing control (SOC) framework was then developed to address uncertainty in problems involving mixed integer formulation. Through case study and scenarios, the approach has proven to be efficient in dealing with uncertainty in crude oil composition.