Process intensification for post combustion CO₂ capture with chemical absorption: a critical review

The concentration of CO₂ in the atmosphere is increasing rapidly. CO₂ emissions may have an impact on global climate change. Effective CO₂ emission abatement strategies such as carbon capture and storage (CCS) are required to combat this trend. Compared with pre-combustion carbon capture and oxy-fuel carbon capture approaches, post-combustion CO₂ capture (PCC) using solvent process is one of the most mature carbon capture technologies. There are two main barriers for the PCC process using solvent to be commercially deployed: (a) high capital cost; (b) high thermal efficiency penalty due to solvent regeneration. Applying process intensification (PI) technology into PCC with solvent process has the potential to significantly reduce capital costs compared with conventional technology using packed columns. This paper intends to evaluate different PI technologies for their suitability in PCC process. The study shows that rotating packed bed (RPB) absorber/stripper has attracted much interest due to its high mass transfer capability. Currently experimental studies on CO₂ capture using RPB are based on standalone absorber or stripper. Therefore a schematic process flow diagram of intensified PCC process is proposed so as to motivate other researches for possible optimal design, operation and control. To intensify heat transfer in reboiler, spinning disc technology is recommended. To replace cross heat exchanger in conventional PCC (with packed column) process, printed circuit heat exchanger will be preferred. Solvent selection for conventional PCC process has been studied extensively. However, it needs more studies for solvent selection in intensified PCC process. The authors also predicted research challenges in intensified PCC process and potential new breakthrough from different aspects

Application of probabilistic deep learning models to simulate thermal power plant processes

Deep learning has gained traction in thermal engineering due to its applications to process simulations, the deeper insights it can provide and its abilities to circumvent the shortcomings of classic thermodynamic simulation approaches by capturing complex inter-dependencies. This works sets out to apply probabilistic deep learning to power plant operations using historic plant data. The first study presented, entails the development of a steady-state mixture density network (MDN) capable of predicting effective heat transfer coefficients (HTC) for the various heat exchanger components inside a utility scale boiler. Selected directly controllable input features, including the excess air ratio, steam temperatures, flow rates and pressures are used to predict the HTCs. In the second case study, an encoder-decoder mixturedensity network (MDN) is developed using recurrent neural networks (RNN) for the prediction of utility-scale air-cooled condenser (ACC) backpressure. The effects of ambient conditions and plant operating parameters, such as extraction flow rate, on ACC performance is investigated. In both case studies, hyperparameter searches are done to determine the best performing architectures for these models. Comparisons are drawn between the MDN model versus standard model architecture in both case studies. The HTC predictor model achieved 90% accuracy which equates to an average error of 4.89 W m2K across all heat exchangers. The resultant time-series ACC model achieved an average error of 3.14 kPa, which translate into a model accuracy of 82%

Application of neural networks to model double tube heat exchangers

Treballs Finals de Grau d'Enginyeria Química, Facultat de Química, Universitat de Barcelona, Curs: 2022-2023, Tutor: David Curcó CantarellArtificial Intelligence is experiencing dramatic growth in recent times. AI models such as ChatGPT have become controversial topics as they continously transform our world. Nevertheless, the true nature of AI is still widely not yet understood by society. Nowadays, Artificial Intelligence is still seen by many as an obscure and foreign concept, even mysterious and threatening. However, this couldn’t be further from the truth. At their essence, they are just mathematical tools which rely on centuries-old knowledge: algebra and calculus. In this project, a neural network model has been created to solve a chemical engineering problem, the predictive model of a double tube heat exchanger. This model is a neural network that predicts future system outputs (inner stream output temperature) from the past values of the input variables of the system (inner and outer streams input temperatures and outer stream flow rate). The data used to train the model was obtained in a simulation written in the Python programming language. Afterwards, the optimal design parameters of the neural network were found experimentally by training different models and testing their performance. This was done in three stages: a proof of concept, a general design stage and a detailed design stage. The model has been successful in predicting the future state of the system with high exactitude while being circa. 3000 times faster than a conventional simulation

An optimized attack tree model for security test case planning and generation

Securing software assets via efficient test case management is an important task in order to realize business goals. Given the huge risks web applications face due to incessant cyberattacks, a proactive risk strategy such as threat modeling is adopted. It involves the use of attack trees for identifying software vulnerabilities at the earliest phase of software development which is critical to successfully protect these applications. Although, many researches have been dedicated to security testing with attack tree models, test case redundancy using this threat modeling technique has been a major issue faced leading to poor test coverage and expensive security testing exercises. This paper presents an attack tree modeling algorithm for deriving a minimal set of effective attack vectors required to test a web application for SQL injection vulnerabilities. By leveraging on the optimized attack tree algorithm used in this research work, the threat model produces efficient test plans from which adequate test cases are derived to ensure a secured web application is designed, implemented and deployed. The experimental result shows an average optimization rate of 41.67% from which 7 test plans and 13 security test cases were designed to mitigate all SQL injection vulnerabilities in the web application under test. A 100% security risk intervention of the web application was achieved with respect to preventing SQL injection attacks after applying all security recommendations from test case execution report

Modelling as Research Methodology

Modelling as Research Methodology is written for the scientist and student researching the (expected) functioning of systems under specified conditions. As such, it represents an introduction to the use of modelling in natural, human and economical sciences. The book is divided into two sections. The first section illustrates the universal nature of modelling as aid to the researcher. In the second section, several typical examples of modelling are described

Modelling HVAC and renewable energy plant and control

The chapter firstly presents an introduction to modern approaches to both steady-state and dynamic modelling of HVAC and related plant components and systems. Approaches to representations of components such as water-air heat exchangers are dealt with and their representation in systems that include control elements are described. Common approaches to solving the resulting equations are also presented. A series of simulation case studies are provided to illustrate the application of the methods. The chapter also presents examples of approaches to modelling the key components of a number of renewable energy systems that can be found integrated in buildings

Harnessing the power of neural networks for the investigation of solar-driven membrane distillation systems under the dynamic operation mode

Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system over 20 days. The predictive capabilities of two neural network models: multilayer perceptron (MLP) and long short-term memory (LSTM) were then comprehensively examined to predict the permeate flux, efficiency and gain-output-ratio (GOR). The results showed that both models can efficiently predict the dynamic performance of solar-driven DCMD systems, where MLP outperformed the LSTM model overall, especially in the prediction of efficiency. Additionally, it was indicated that the accuracy of the models for the prediction of GOR can be significantly improved by increasing the size of the dataset

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