Synthesis of optimal heat and mass exchange networks using a two-step hybrid approach including detailed unit designs

This PhD thesis develops a methodology for the synthesis of optimal heat and mass exchanger networks through a novel hybrid method. The two-step procedure makes use of simplified exchanger models in a network optimisation step, followed by a detailed design where the exchangers found in the network synthesis step are modelled in detail. Subsequent iterations of the network design step are then updated with information from the detailed network designs. The algorithm has certain advantages over previous methods in that the network optimisation is based on more realistic representations of the actual units therein and also that the method increases the likelihood of attaining a globally optimal solution through the generation and assessment of multiple candidate networks throughout the algorithm. The method can be used in a variety of applications and is demonstrated to be effective for large problems and multi-period scenarios. The thesis also shows that the method can be used in conjunction with multiple individual unit optimisation techniques including heuristics and fully explicit optimisation methods

A Simple Method for Finding Optimal Paths of Hot and Cold Streams inside Shell and Tube Heat Exchangers to Reduce Pumping Cost in Heat Exchanger Network Problems

In this paper, a simple method is presented for the synthesis and retrofit of heat exchanger networks (HENs) by considering pressure drop as well as finding proper path of streams inside heat exchangers (HEs) to reduce the pumping cost of network. Generally, HEN problems lead to MINLP models which have convergence difficulties due to the existence of both continuous and integer variables. In this study, instead of solving these variables simultaneously, a combination of Genetic Algorithm (GA) with Quasi Linear Programming (QLP) and Integer Linear Programming (ILP) was used for solving the problem. GA was used to find optimal HENs structure and streams paths, whereas continuous variables were solved by QLP. For the retrofit of HENs, a modified ILP model was used. Results show that the proposed method has the ability to reduce the cost of annual pumping due to considering optimal paths for streams in the HEs compared to the literature. This work is licensed under a Creative Commons Attribution 4.0 International License

Optimization of refinery preheat trains undergoing fouling: control, cleaning scheduling, retrofit and their integration

Crude refining is one of the most energy intensive industrial operations. The large amounts of crude processed, various sources of inefficiencies and tight profit margins promote improving energy recovery. The preheat train, a large heat exchanger network, partially recovers the energy of distillation products to heat the crude, but it suffers of the deposition of material over time – fouling – deteriorating its performance. This increases the operating cost, fuel consumption, carbon emissions and may reduce the production rate of the refinery. Fouling mitigation in the preheat train is essential for a profitable long term operation of the refinery. It aims to increase energy savings, and to reduce operating costs and carbon emissions. Current alternatives to mitigate fouling are based on heuristic approaches that oversimplify the representation of the phenomena and ignore many important interactions in the system, hence they fail to fully achieve the potential energy savings. On the other hand, predictive first principle models and mathematical programming offer a comprehensive way to mitigate fouling and optimize the performance of preheat trains overcoming previous limitations. In this thesis, a novel modelling and optimization framework for heat exchanger networks under fouling is proposed, and it is based on fundamental principles. The models developed were validated against plant data and other benchmark models, and they can predict with confidence the main effect of operating variables on the hydraulic and thermal performance of the exchangers and those of the network. The optimization of the preheat train, an MINLP problem, aims to minimize the operating cost by: 1) dynamic flow distribution control, 2) cleaning scheduling and 3) network retrofit. The framework developed allows considering these decisions individually or simultaneously, although it is demonstrated that an integrated approach exploits the synergies among decision levels and can reduce further the operating cost. An efficient formulation of the model disjunctions and time representation are developed for this optimization problem, as well as efficient solution strategies. To handle the combinatorial nature of the problem and the many binary decisions, a reformulation using complementarity constraints is proposed. Various realistic case studies are used to demonstrate the general applicability and benefits of the modelling and optimization framework. This is the first time that first principle predictive models are used to optimize various types of decisions simultaneously in industrial size heat exchanger networks. The optimization framework developed is taken further to an online application in a feedback loop. A multi-loop NMPC approach is designed to optimize the flow distribution and cleaning scheduling of preheat trains over two different time scales. Within this approach, dynamic parameter estimation problems are solved at frequent intervals to update the model parameters and cope with variability and uncertainty, while predictive first principle models are used to optimize the performance of the network over a future horizon. Applying this multi-loop optimization approach to a case study of a real refinery demonstrates the importance of considering process variability on deciding about optimal fouling mitigation approaches. Uncertainty and variability have been ignored in all previous model based fouling mitigation strategies, and this novel multi-loop NMPC approach offers a solution to it so that the economic savings are enhanced. In conclusion, the models and optimization algorithms developed in this thesis have the potential to reduce the operating cost and carbon emission of refining operations by mitigating fouling. They are based on accurate models and deterministic optimization that overcome the limitations of previous applications such as poor predictability, ignoring variability and dynamics, ignoring interactions in the system, and using inappropriate tools for decision making.Open Acces

New Elements of Heat Transfer Efficiency Improvement in Systems and Units

Zvýšení efektivity výměny tepla vede k poklesu spotřeby energie, což se následně projeví sníženými provozními náklady, poklesem produkce emisí a potažmo také snížením dopadu na životní prostředí. Běžné způsoby zefektivňování přenosu tepla jako např. přidání žeber či vestaveb do trubek ovšem nemusí být vždy vhodné nebo proveditelné -- zvláště při rekuperaci tepla z proudů s vysokou zanášivostí. Jelikož intenzita přestupu tepla závisí i na charakteru proudění, distribuci toku a zanášení, které lze všechny výrazně ovlivnit tvarem jednotlivých součástí distribučního systému, bylo sestaveno několik zjednodušených modelů pro rychlou a dostatečně přesnou predikci distribuce a také aplikace pro tvarovou optimalizaci distribučních systémů využívající právě tyto modely. Přesnost jednoho z modelů byla dále zvýšena pomocí dat získaných analýzou 282 distribučních systémů v softwaru ANSYS FLUENT. Vytvořené aplikace pak lze využít během návrhu zařízení na výměnu tepla ke zvýšení jejich výkonu a spolehlivosti.Improved heat transfer efficiency leads to decrease in energy consumption which then results in lower equipment operational cost, reduced emissions, and consequently also lower environmental impact. However, common enhancement approaches such as adding fins or tube inserts may not always be suitable or feasible -- especially in case of heat recovery from streams having a high fouling propensity. Since heat transfer rate depends also on flow field characteristics, fluid distribution, and fouling which can all be greatly influenced by the actual shapes of flow system components, several simplified models for fast and accurate enough prediction of fluid distribution as well as applications for shape optimization based on these models were developed. In addition, accuracy of one of the models was further increased by fine-tuning it using data obtained by evaluation of 282 flow systems in the fluid flow modelling software ANSYS FLUENT. The created applications can then be employed during the design of heat exchange units to improve their performance and reliability.

The synthesis of Combined Heat and Mass Exchange Networks (CHAMENs) with renewables considering environmental impact

Process synthesis is used to evaluate different potential designs to select the most suitable that fulfils some process goals. There is ever-increasing pressure to reduce operating cost and emission of pollutants as energy prices continue to increase and more regulations are set by government. To address these concerns, optimisation methods based on heuristics, pinch technology and mathematical programming can be adopted. Since the early 90s, mathematical programming has gained significant attention to solve large and complex problems. Extensive studies have been conducted for heat exchange network synthesis (HENS), which was first used to optimise utility usage and operating costs. Many existing mass exchange network synthesis (MENS) methods are derived from HENS techniques since analogies exist between the two networks. Integrating the synthesis of mass and heat exchange networks in what is known as combined heat and mass exchange network synthesis (CHAMENS) can be beneficial because mass transfer is affected by operating temperature. However, very little research has been done in this area of process synthesis due to their complex nature. It is even more challenging to find literature involving the regeneration of multiple recyclable MSAs in a network synthesis context. Furthermore, the few studies that have considered CHAMENS have done the optimisation considering economic performance alone, whereas the consideration of environmental impact as an additional objective can help attain a more sustainable process. This thesis builds on current knowledge of CHAMENs synthesis methods by considering CHAMENs with detailed regeneration networks (RENs) involving multiple recyclable MSAs, multiple regenerating streams, and solar thermal as an alternative energy source, using a multi-period synthesis approach. Simultaneously optimising a combination of these networks is not a trivial task due to the resulting large model size having many binary and non-linear terms and the interactions among them. Stage-wise superstructure (SWS) synthesis approaches for heat and mass exchanger networks are adopted in this thesis for the synthesis of CHAMENs. A new superstructure for RENs, which is equivalent to that of a MEN, is presented in this thesis. The combined superstructure, which involves multiple MSAs, multiple regenerants, and multiple hot and cold process streams, is integrated with solar thermal energy as a renewable energy option. The availability of solar thermal energy is simplified by discretizing into two time periods of daytime and nighttime operations. The proposed CHAMEN model is also extended to handle multi-objective optimisation (MOO) of environmental impact and economic objectives to identify the optimal network configuration. Two examples were solved, and the results obtained showed that the implementation of integrated solar panels and thermal storage tanks could reduce the environmental impact of the combined networks by 76% and 26% for case studies 1 and 2 respectively. However, such eco-friendly infrastructure resulted in increased total annual cost (TAC) values of 36% and 15% respectively for the two case studies. These results indicate that by using the methodologies developed in this thesis, trade-offs can be established between economics and environmental impact as objectives