Advancing Carbon Sequestration through Smart Proxy Modeling: Leveraging Domain Expertise and Machine Learning for Efficient Reservoir Simulation

Geological carbon sequestration (GCS) offers a promising solution to effectively manage extra carbon, mitigating the impact of climate change. This doctoral research introduces a cutting-edge Smart Proxy Modeling-based framework, integrating artificial neural networks (ANNs) and domain expertise, to re-engineer and empower numerical reservoir simulation for efficient modeling of CO2 sequestration and demonstrate predictive conformance and replicative capabilities of smart proxy modeling. Creating well-performing proxy models requires extensive human intervention and trial-and-error processes. Additionally, a large training database is essential to ANN model for complex tasks such as deep saline aquifer CO2 sequestration since it is used as the neural network\u27s input and output data. One major limitation in CCS programs is the lack of real field data due to a lack of field applications and issues with confidentiality. Considering these drawbacks, and due to high-dimensional nonlinearity, heterogeneity, and coupling of multiple physical processes associated with numerical reservoir simulation, novel research to handle these complexities as it allows for the creation of possible CO2 sequestration scenarios that may be used as a training set. This study addresses several types of static and dynamic realistic and practical field-base data augmentation techniques ranging from spatial complexity, spatio-temporal complexity, and heterogeneity of reservoir characteristics. By incorporating domain-expertise-based feature generation, this framework honors precise representation of reservoir overcoming computational challenges associated with numerical reservoir tools. The developed ANN accurately replicated fluid flow behavior, resulting in significant computational savings compared to traditional numerical simulation models. The results showed that all the ML models achieved very good accuracies and high efficiency. The findings revealed that the quality of the path between the focal cell and injection wells emerged as the most crucial factor in both CO2 saturation and pressure estimation models. These insights significantly contribute to our understanding of CO2 plume monitoring, paving the way for breakthroughs in investigating reservoir behavior at a minimal computational cost. The study\u27s commitment to replicating numerical reservoir simulation results underscores the model\u27s potential to contribute valuable insights into the behavior and performance of CO2 sequestration systems, as a complimentary tool to numerical reservoir simulation when there is no measured data available from the field. The transformative nature of this research has vast implications for advancing carbon storage modeling technologies. By addressing the computational limitations of traditional numerical reservoir models and harnessing the synergy between machine learning and domain expertise, this work provides a practical workflow for efficient decision-making in sequestration projects

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

State Estimation in Lithium-ion Batteries Using Pulse Perturbation and Feedforward Neural Networks

Predicting battery stored charge, available capacity, and peak power quickly and accurately is important for understanding pack performance and stability. It is proposed that a feedforward neural network (FNN) can estimate this information using cell voltage response to an injected current. Voltage response varies with the internal chemistry, represented by charge, capacity, and impedance. These characteristics are quantified here using state of charge (SoC), state of energy (SoE), and state of power (SoP). Cell response data is collected for various states at constant temperature, resulting in 234 unique voltage responses for training and evaluating the FNN. Training is performed using 3 distinct variations on the data: (1) the full voltage response, (2) individual portions of the response, such as charging or relaxation periods, and (3) fractions of the charge and discharge periods ranging from one-half to a single open-circuit voltage measurement. Using the full response, the average mean absolute error (MAE) is 0.0057 for SoE estimation. The average MAE is below 0.0080 for SoC and SoP estimation. The results for pulse portions show that Charge-rest or Discharge-rest responses perform almost as well as the full pulse. This may inform future pulse design for further optimization. The results for pulse fractions show that error increases as the amount of input data decreases, which validates the hypothesis that pulse perturbation yields high performance in FNN. The technique can be expanded to other temperatures, with potential for estimation of other states, and even degradation mechanisms. Estimation requires 3 minutes of voltage and current data, with no charging history needed and low computational complexity. The proposed method is thus suitable for development of advanced battery management systems in electric vehicles

Technology, Science, and Culture: A Global Vision

The aim of the Workshop: Technology, Science, and Culture - A Global Vision is to create a discussion forum on research related to the fields of Water Science, Food Science, Intelligent Systems, Molecular Biomedicine, and Creation and Theories of Culture. The workshop is intended to discuss research on current problems, relevant methodologies, and future research streams and to create an environment for the exchange of ideas and collaboration among participants

Supply chain inventory control for the iron and steel industry

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Adaptive Railway Traffic Control using Approximate Dynamic Programming

Railway networks around the world have become challenging to operate in recent decades, with a mixture of track layouts running several different classes of trains with varying operational speeds. This complexity has come about as a result of the sustained increase in passenger numbers where in many countries railways are now more popular than ever before as means of commuting to cities. To address operational challenges, governments and railway undertakings are encouraging development of intelligent and digital transport systems to regulate and optimise train operations in real-time to increase capacity and customer satisfaction by improved usage of existing railway infrastructure. Accordingly, this thesis presents an adaptive railway traffic control system for realtime operations based on a data-based approximate dynamic programming (ADP) approach with integrated reinforcement learning (RL). By assessing requirements and opportunities, the controller aims to reduce delays resulting from trains that entered a control area behind schedule by re-scheduling control plans in real-time at critical locations in a timely manner. The present data-based approach depends on an approximation to the value function of dynamic programming after optimisation from a specified state, which is estimated dynamically from operational experience using RL techniques. By using this approximation, ADP avoids extensive explicit evaluation of performance and so reduces the computational burden substantially. In this thesis, formulations of the approximation function and variants of the RL learning techniques used to estimate it are explored. Evaluation of this controller shows considerable improvements in delays by comparison with current industry practices