Modelling of a post-combustion CO2 capture process using extreme learning machine

This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine (ELM). ELM randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This feature allows an ELM model being developed very quickly. This paper proposes using principal component regression to obtain the weights between the hidden and output layers to address the collinearity issue among hidden neuron outputs. Due to the weights between input and hidden layers are randomly assigned, ELM models could have variations in performance. This paper proposes combining multiple ELM models to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, eight parameters in the process were utilized as model input variables: inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flow rate, lean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process

Machine learning based modelling and optimization of post-combustion carbon capture process using MEA supporting carbon neutrality

The role of carbon capture technology using monoethanolamine (MEA) is critical for achieving the carbon-neutrality goal. However, maintaining the efficient operation of the post-combustion carbon capture is challenging considering the hyperdimensional design space and nonlinear characteristics of the process. In this work, CO2 capture level from the flue gas in the absorption column is investigated for the post-combustion carbon capture process using MEA. Artificial neural network (ANN) and support vector machine (SVM) models are constructed to model CO2 capture level under extensive hyperparameters tuning. The comparative performance analysis based on external validation test confirmed the superior modelling and generalization ability of ANN for the carbon capture process. Later, partial derivative-based sensitivity analysis is carried out and it is the found that absorbent-based input variables like lean solvent temperature and lean solvent flow rate are the two most significant input variables on CO2 capture level in the absorption column. The optimization problem with the ANN model embedded in the nonlinear programming-based optimization environment is solved under different operating scenarios to determine the optimum operating ranges for the input variables corresponding to the maximum CO2 capture level. This research presents the optimum operating conditions for CO2 removal from the flue gas for the post-combustion carbon capture process using MEA that contributes to achieving the carbon neutrality goal

Modelling of a post-combustion CO2 capture process using deep belief network

This paper presents a study on using deep learning for the modelling of a post-combustion CO 2 capture process. Deep learning has emerged as a very powerful tool in machine learning. Deep learning technique includes two phases: an unsupervised pre-training phase and a supervised back-propagation phase. In the unsupervised pre-training phase, a deep belief network (DBN) is pre-trained to obtain initial weights of the subsequent supervised phase. In the supervised back-propagation phase, the network weights are fine-tuned in a supervised manner. DBN with many layers of Restricted Boltzmann Machine (RBM) can extract a deep hierarchical representation of training data. In terms of the CO 2 capture process, the DBN model predicts CO 2 production rate and CO 2 capture level using the following variables as model inputs: inlet flue gas flow rate, CO 2 concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. A greedy layer-wise unsupervised learning algorithm is introduced to optimize DBN, which can bring better generalization than a single hidden layer neural network. The developed deep architecture network models can then be used in the optimisation of the CO 2 capture process

Modelling and optimisation of post-combustion carbon capture process integrated with coal-fired power plant using computational intelligence techniques

PhD ThesisCoal-fired power plants are the major source of CO2 emission which contributes significantly to global climate change. An effective way to reduce CO2 emission in coal-fired power plants is post-combustion carbon dioxide (CO2) capture (PCC) with chemical absorption. The aim of this project is to carry out some research in model development, process analysis, controller design and process optimization for reliable, optimal design and control of coal-fired supercritical power plant integrated with post-combustion carbon capture plant. In this thesis, three different advanced neural network models are developed: bootstrap aggregated neural networks (BANNs) model, bootstrap aggregated extreme learning machine (BAELM) model and deep belief networks (DBN) model. The bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. However, both BANNs and BAELM have a shallow architecture, which is limited to represent complex, highly-varying relationship and easy to converge to local optima. To resolve the problem, the DBN model is proposed. The unsupervised training procedure is helpful to get the optimal solution of supervised training. The purpose of developing neural network models is to find a best model which can be used in the optimization of the CO2 capture process precisely. This thesis also presents a comparison of centralized and decentralized control structures for post-combustion CO2 capture plant with chemical absorption. As for centralized configuration, a dynamic multivariate model predictive control (MPC) technique is used to control the post-combustion CO2 capture plant attached to a coal-fired power plant. When consider the decentralized control structures based on multi-loop proportional-integral-derivative (PID) controllers, two different control schemes are designed using relative disturbance gain (RDG) analysis and dynamic relative gain array (DRGA) analysis, respectively. By comparing the two control structures, the MPC structure performs better in terms of closed-loop settling time, integral squared error, and disturbance injection

Environmental, health and safety assessment of phase-change solvents for post combustion CO2 capture

A novel class of solvents exhibiting liquid-liquid phase separation upon reaction with CO2 and/or change in temperature, promises significant reduction of energy requirement of the post combustion capture by chemical absorption. However, proceeding to a large-scale application of novel materials requires holistic evaluation of the aspects related to human health, safety, and environmental impacts currently missing for phase-change solvent alternatives. The current work addresses the gap by performing such an evaluation by help of combined life cycle (LCA) and environmental, health and safety hazard (EHS) assessment. The evaluation is done at the substance level, during the process of design and selection of the solvent alternatives by computer-aided molecular design (CAMD), and the process level, estimating the impact of the capture system deploying phase-change solvents. The integration of the LCA and EHS impact criteria into the solvent design procedure leads to identification of a much wider set of optimal solvent structures compared to having only thermodynamic properties as objective functions in CAMD. The search enriched the Pareto fronts with the -OH group containing structures beneficial in terms of their lower impact. On one hand, such molecules are highly soluble in water, thus they might not be the best option from the phase-change perspective. On the other hand, there are OH-containing amines proven to exhibit liquid-liquid separation, which have so far received considerably less attention and might require further investigation.The process level assessment showed that phase-change solvent systems have a potential to be a better alternative to the conventional amine solvent systems due to the reduced reboiler duty and possible lower impact on the environment. Less mobile solvents might be preferable with respect to human safety. With respect to long-term impacts, the process design of the capture systems with the phase-change solvents might promote accumulation of carcinogenic nitrosamines, thus their concentration should be monitored. The life cycle impact was mostly defined by the steam requirement for solvent regeneration and electricity demand for cooling media delivery. The use of renewable electricity and industrial waste heat can decrease the LCA impact of the phase-change capture plant by 70-90%. Then, the remaining impact will be dominated by the degradation behaviour of the solvent molecules, which emphasizes the benefit of the solvents displaying low degradation rates and highlights the importance of experimental studies addressing the degradation behaviour of the solvents

Non-linear system Identification and control of Solvent-Based Post-Combustion CO2 Capture Process

Solvent-based post-combustion capture (PCC) is a well-developed technology for CO2 capture from power plants and industry. A reliable model that captures the dynamics of the solvent-based capture process is essential to implement suitable control system design. Typically, first principles models are used, however, they usually require comprehensive knowledge and in-depth understanding of the process. In addition, the high computational time required and high complexity of the first principles models makes it unsuitable for control system design implementation. This thesis is aimed at the development of a reliable dynamic model via system identification technique as well as a suitable process control strategy for the solvent-based post-combustion CO2 capture process. The nonlinear autoregressive with exogenous (NARX) inputs model is employed to represent the relationship between the input variables and output variables as two multiple-input single-output (MISO) sub-systems. The forward regression with orthogonal least squares (FROLS) algorithm is implemented to select an accurate model structure that best describes the dynamics within the process. The prediction performance of the identified NARX models is promising and shows that the models capture the underlying dynamics of the CO2 capture process. The model obtained was adopted for various process control system design of the solvent-based PCC process (conventional PI, MPC, and NMPC). For the conventional PI controller design, multivariable control analysis was carried out to determine a suitable control structure. Control performance evaluation of the control schemes reveals that the NMPC scheme was suitable to control the solvent-based PCC process at flexible operations. Findings obtained from the thesis underlines the advancement in dynamic modelling and control implementation of solvent-based PCC process

AI-driven optimization of ethanol-powered internal combustion engines in alignment with multiple SDGs: A sustainable energy transition

With the escalating requirement for global sustainable energy solutions and the complexities linked with the complete transition to new technologies, internal combustion engines (ICEs) powered with biofuels like ethanol are gaining significance over time. However, problems linked to the performance and emissions of such ICEs necessitate accurate prediction and optimization. The study employed the integration of artificial neural networks (ANN) and multi-level historical design of response surface methodology (RSM) to address these challenges in alignment with the Sustainable Development Goals (SDGs). A single-cylinder spark ignition (SI) engine powered with ethanol-gasoline blends at different loads and speeds was used to gather data. Among six initially trained ANN models, the most efficient model with a regression coefficient (R2) of 0.9952 (training), 0.98579 (validation), 0.98847 (testing), and 0.99307 (overall) was employed to predict outputs such as brake power, brake specific fuel consumption (BSFC), brake thermal energy (BTE), concentration of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen NOx. Predicted outputs were optimized by incorporating RSM. On implementing optimized conditions, it was observed that BP and BTE increased by 19.9%, and 29.8%, respectively. Additionally, CO, and HC emissions experienced substantial reductions of 28.1%, and 40.6%, respectively. This research can help engine producers and researchers make refined decisions and achieve improved performance and emissions. The study directly supports SDG 7, SDG 9, SDG 12, SDG 13, and SGD 17, which call for achieving affordable, clean energy, sustainable industrialization, responsible consumption, and production, taking action on climate change, and partnership to advance the SDGs as a whole respectively

Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems

Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful computational method for fundamental research in science branches such as biology, chemistry, biomedicine and physics over the past 60 years. Powered by rapidly advanced supercomputing technologies in recent decades, MD has entered the engineering domain as a first-principle predictive method for material properties, physicochemical processes, and even as a design tool. Such developments have far-reaching consequences, and are covered for the first time in the present paper, with a focus on MD for combustion and energy systems encompassing topics like gas/liquid/solid fuel oxidation, pyrolysis, catalytic combustion, heterogeneous combustion, electrochemistry, nanoparticle synthesis, heat transfer, phase change, and fluid mechanics. First, the theoretical framework of the MD methodology is described systemically, covering both classical and reactive MD. The emphasis is on the development of the reactive force field (ReaxFF) MD, which enables chemical reactions to be simulated within the MD framework, utilizing quantum chemistry calculations and/or experimental data for the force field training. Second, details of the numerical methods, boundary conditions, post-processing and computational costs of MD simulations are provided. This is followed by a critical review of selected applications of classical and reactive MD methods in combustion and energy systems. It is demonstrated that the ReaxFF MD has been successfully deployed to gain fundamental insights into pyrolysis and/or oxidation of gas/liquid/solid fuels, revealing detailed energy changes and chemical pathways. Moreover, the complex physico-chemical dynamic processes in catalytic reactions, soot formation, and flame synthesis of nanoparticles are made plainly visible from an atomistic perspective. Flow, heat transfer and phase change phenomena are also scrutinized by MD simulations. Unprecedented details of nanoscale processes such as droplet collision, fuel droplet evaporation, and CO2 capture and storage under subcritical and supercritical conditions are examined at the atomic level. Finally, the outlook for atomistic simulations of combustion and energy systems is discussed in the context of emerging computing platforms, machine learning and multiscale modelling

Leveraging Artificial Intelligence and Geomechanical Data for Accurate Shear Stress Prediction in CO2 Sequestration within Saline Aquifers (Smart Proxy Modeling)

This research builds upon the success of a previous project that used a Smart Proxy Model (SPM) to predict pressure and saturation in Carbon Capture and Storage (CCS) operations into saline aquifers. The Smart Proxy Model is a data-driven machine learning model that can replicate the output of a sophisticated numerical simulation model for each time step in a short amount of time, using Artificial Intelligence (AI) and large volumes of subsurface data. This study aims to develop the Smart Proxy Model further by incorporating geomechanical datadriven techniques to predict shear stress by using a neural network, specifically through supervised learning, to construct Smart Proxy Models, which are critical to ensuring the safety and effectiveness of Carbon Capture and Storage operations. By training the Smart Proxy Model with reservoir simulations that incorporate varying geological properties and geomechanical data, we will be able to predict the distribution of shear stress. The ability to accurately predict shear stress is crucial to mitigating the potential risks associated with Carbon Capture and Storage operations. The development of a geomechanical Smart Proxy Model will enable more efficient and reliable subsurface modeling decisions in Carbon Capture and Storage operations, ultimately contributing to the safe and effective storage of CO2 and the global effort to combat climate change

Sustainability and Safety Study of Tank to Propeller Process

Many public concerns have been brought to the increasingly intense greenhouse effects. The International Maritime Organization (IMO) has ambitious strategies to limit the air pollutant emissions from the merchant ships in a long run, especially for carbon, sulfur, methane and nitrogen oxides. To achieve IMO 2050 decarbonization objectives, more than one solution are required for maritime energy transition, from electric batteries for onboard activities to a variety of “green fuels” as well as safe and sustainable process design of onboard carbon capture, utilization, and storage (CCUS). Our work is focusing on screening promising marine fuels and providing safer and more sustainable carbon capture systems for maritime industry from the perspective of process safety and process systems engineering. This work can be divided into four major parts: Tank to propeller (TTP) sustainability study focuses on providing solutions on marine fuel consumption and TTP exhaust gas emission control, and a bottom-up emission inventory model was developed by analyzing and optimizing multiple parameters; Then an onboard carbon capture system called TTP post-combustion carbon capture (TTPPCC) system was proposed by integrating ship engine process modeling with chemical absorption/desorption process modeling techniques, this work covers a thorough sustainability evaluation based on emission reduction efficiency, energy penalty, and carbon cyclic capacity among two single aqueous amines, MEA and diisopropanolamine (DIPA), and one blended amine with a promoter, methyldiethanolamine (MDEA) with piperazine (PZ); The first TTP safety study aims at identifying the contributors influencing liquid aerosol flammability and solving their data deficiencies by developing quantitative structure−property relationship (QSPR) models, 1215 liquid chemicals and 14 predictors have been input to train the developed machine learning models via k-fold cross validation with the consideration of principal component analysis; The second TTP process safety study makes contributions on exploring inherently safer marine fuels by offering a liquid combustion risk criterion for ship compression ignition engines, two unsupervised machine learning clustering models were developed by considering liquid flammability flame propagation and aerosol formulation characteristics