Browsing by Author "Liu, Yih-An"

Now showing 1 - 7 of 7
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    Catalytic Hydrodeoxygenation of Bio-Oil Model Compounds (Ethanol, 2-Methyltetrahydrofuran) over Supported Transition Metal Phosphides
    Bui, Phuong Phuc Nam (Virginia Tech, 2013-01-24)
    The objective of this project is to investigate hydrodeoxygenation (HDO), a crucial step in the treatment of bio-oil, on transition metal phosphide catalysts. The study focuses on reactions of simple oxygenated compounds present in bio-oil -- ethanol and 2-methyltetrahydrofuran (2-MTHF). The findings from this project provide fundamental knowledge towards the hydrodeoxygenation of more complex bio-oil compounds. Ultimately, the knowledge contributes to the design of optimum catalysts for upgrading bio-oil. A series of transition metal phosphides was prepared and tested; however, the focus was on Ni2P/SiO2. Characterization techniques such as X-ray diffraction (XRD), temperature-programmed reduction and desorption (TPR and TPD), X-ray photoelectron spectroscopy (XPS), and chemisorption were used. In situ Fourier transform infrared (FTIR) spectroscopy was employed to monitor the surface of Ni2P during various experiments such as: CO and pyridine adsorption and transient state of ethanol and 2-MTHF reactions. The use of these techniques allowed for a better understanding of the role of the catalyst during deoxygenation.
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    Design and Optimization of Post-Combustion CO2 Capture
    Higgins, Stuart James (Virginia Tech, 2016-05-17)
    This dissertation describes the design and optimization of a CO2-capture unit using aqueous amines to remove of carbon dioxide from the flue gas of a coal-fired power plant. In particular we construct a monolithic model of a carbon capture unit and conduct a rigorous optimization to find the lowest solvent regeneration energy yet reported. Carbon capture is primarily motivated by environmental concerns. The goal of our work is to help make carbon capture and storage (CCS) a more efficient for the sort of universal deployment called for by the Intergovernmental Panel on Climate Change (IPCC) to stabilize anthropomorphic contributions to climate change, though there are commercial applications such as enhanced oil recovery (EOR). We employ the latest simulation tools from Aspen Tech to rigorously model, design, and optimize acid gas systems. We extend this modeling approach to leverage Aspen Plus in the .NET framework through Microsoft's Component Object Model (COM). Our work successfully increases the efficiency of acid gas capture. We report a result optimally implementing multiple energy-saving schemes to reach a thermal regeneration energy of 1.67 GJ/tonne. By contrast, the IPCC had reported that leading technologies range from 2.7 to 3.3 GJ/tonne in 2005. Our work has received significant endorsement for industrial implementation by the senior management from the world's second largest chemical corporation, Sinopec, as being the most efficient technology known today.
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    Development of Computational Tools for the Design, Simulation and Optimization of Cyclic Steady State (CSS) Adsorption and Chromatographic Processes
    Wood, Kevin (Virginia Tech, 2016-08-26)
    This dissertation presents an analysis of two aspects of the chromatographic separation process known as Simulated Moving Bed (SMB) chromatography. The first aspect is system design, and the second is improving computer simulations to generate heuristics for choosing operational modes. For the past 15-20 years, there has been a surge of interest in the use of Simulated Moving Bed systems for the chromatographic separation of chemicals¹. A wide variety of methods, nomenclatures, and conventions have been adopted over the years²⁻⁴, as teams from different backgrounds adopt and improve on the SMB technology. This work presents a unifying discussion of the two major design methods, Triangle Theory and Standing Wave Design, used in the SMB field. We provide the complete computer code required to execute both design methods. A sample problem is worked, which demonstrates the novelty and ease of use that such tools provide. Mathematica was chosen for the implementation of these design methods, because of its strong symbolic analysis capabilities, and simplicity of creating interfaces for new users. We present derivations of the classic Langmuir results in Mathematica, and proceed to extend those implementations. When analytic solutions are impossible, we use Mathematica's numerical methods. This work also develops a distributed computing tool known as ChromRunner which allows large numbers of detailed numerical simulations to be run simultaneously. The motivations and benefits of this approach are discussed alongside implementation details. We apply the distributed computing system to two separate SMB separations in order to optimize them, as well as determine heuristics governing their operational modes. We wrote ChromRunner in C#, and took advantage of Visual Studio's Entity Framework to create the database backend. The user interface for this software was created using Microsoft's "Windows Presentation Foundation" (WPF) technologies.
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    Energy-efficient extraction of acid gas from flue gases
    (United States Patent and Trademark Office, 2020-08-25)
    An energy-efficient method of removing carbon dioxide, hydrogen sulfide, and other acid gases from a stream of flue gases. The flue stream is contacted with a predetermined sorbent system to remove acid gases from the flue stream. The acid gas-rich sorbent is then heated to desorb the acid gas for capture and regenerate the sorbent. Heat exchangers and heat pumps are used to reduce utility steam and/or cooling water consumption.
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    Integrated Process Modeling and Data Analytics for Optimizing Polyolefin Manufacturing
    Sharma, Niket (Virginia Tech, 2021-11-19)
    Polyolefins are one of the most widely used commodity polymers with applications in films, packaging and automotive industry. The modeling of polymerization processes producing polyolefins, including high-density polyethylene (HDPE), polypropylene (PP), and linear low-density polyethylene (LLDPE) using Ziegler-Natta catalysts with multiple active sites, is a complex and challenging task. In our study, we integrate process modeling and data analytics for improving and optimizing polyolefin manufacturing processes. Most of the current literature on polyolefin modeling does not consider all of the commercially important production targets when quantifying the relevant polymerization reactions and their kinetic parameters based on measurable plant data. We develop an effective methodology to estimate kinetic parameters that have the most significant impacts on specific production targets, and to develop the kinetics using all commercially important production targets validated over industrial polyolefin processes. We showcase the utility of dynamic models for efficient grade transition in polyolefin processes. We also use the dynamic models for inferential control of polymer processes. Thus, we showcase the methodology for making first-principle polyolefin process models which are scientifically consistent, but tend to be less accurate due to many modeling assumptions in a complex system. Data analytics and machine learning (ML) have been applied in the chemical process industry for accurate predictions for data-based soft sensors and process monitoring/control. Specifically, for polymer processes, they are very useful since the polymer quality measurements like polymer melt index, molecular weight etc. are usually less frequent compared to the continuous process variable measurements. We showcase the use of predictive machine learning models like neural networks for predicting polymer quality indicators and demonstrate the utility of causal models like partial least squares to study the causal effect of the process parameters on the polymer quality variables. ML models produce accurate results can over-fit the data and also produce scientifically inconsistent results beyond the operating data range. Thus, it is growingly important to develop hybrid models combining data-based ML models and first-principle models. We present a broad perspective of hybrid process modeling and optimization combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach and not just the direct combinations of first-principle and ML models. We present a detailed review of scientific literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models according to their methodology and objective. We identify the themes and methodologies which have not been explored much in chemical engineering applications, like the use of scientific knowledge to help improve the ML model architecture and learning process for more scientifically consistent solutions. We apply these hybrid SGML techniques to industrial polyolefin processes such as inverse modeling, science guided loss and many others which have not been applied previously to such polymer applications.
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    Machine Learning and Multivariate Statistics for Optimizing Bioprocessing and Polyolefin Manufacturing
    Agarwal, Aman (Virginia Tech, 2022-01-07)
    Chemical engineers have routinely used computational tools for modeling, optimizing, and debottlenecking chemical processes. Because of the advances in computational science over the past decade, multivariate statistics and machine learning have become an integral part of the computerization of chemical processes. In this research, we look into using multivariate statistics, machine learning tools, and their combinations through a series of case studies including a case with a successful industrial deployment of machine learning models for fermentation. We use both commercially-available software tools, Aspen ProMV and Python, to demonstrate the feasibility of the computational tools. This work demonstrates a novel application of ensemble-based machine learning methods in bioprocessing, particularly for the prediction of different fermenter types in a fermentation process (to allow for successful data integration) and the prediction of the onset of foaming. We apply two ensemble frameworks, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to build classification and regression models. Excessive foaming can interfere with the mixing of reactants and lead to problems, such as decreasing effective reactor volume, microbial contamination, product loss, and increased reaction time. Physical modeling of foaming is an arduous process as it requires estimation of foam height, which is dynamic in nature and varies for different processes. In addition to foaming prediction, we extend our work to control and prevent foaming by allowing data-driven ad hoc addition of antifoam using exhaust differential pressure as an indicator of foaming. We use large-scale real fermentation data for six different types of sporulating microorganisms to predict foaming over multiple strains of microorganisms and build exploratory time-series driven antifoam profiles for four different fermenter types. In order to successfully predict the antifoam addition from the large-scale multivariate dataset (about half a million instances for 163 batches), we use TPOT (Tree-based Pipeline Optimization Tool), an automated genetic programming algorithm, to find the best pipeline from 600 other pipelines. Our antifoam profiles are able to decrease hourly volume retention by over 53% for a specific fermenter. A decrease in hourly volume retention leads to an increase in fermentation product yield. We also study two different cases associated with the manufacturing of polyolefins, particularly LDPE (low-density polyethylene) and HDPE (high-density polyethylene). Through these cases, we showcase the usage of machine learning and multivariate statistical tools to improve process understanding and enhance the predictive capability for process optimization. By using indirect measurements such as temperature profiles, we demonstrate the viability of such measures in the prediction of polyolefin quality parameters, anomaly detection, and statistical monitoring and control of the chemical processes associated with a LDPE plant. We use dimensionality reduction, visualization tools, and regression analysis to achieve our goals. Using advanced analytical tools and a combination of algorithms such as PCA (Principal Component Analysis), PLS (Partial Least Squares), Random Forest, etc., we identify predictive models that can be used to create inferential schemes. Soft-sensors are widely used for on-line monitoring and real-time prediction of process variables. In one of our cases, we use advanced machine learning algorithms to predict the polymer melt index, which is crucial in determining the product quality of polymers. We use real industrial data from one of the leading chemical engineering companies in the Asia-Pacific region to build a predictive model for a HDPE plant. Lastly, we show an end-to-end workflow for deep learning on both industrial and simulated polyolefin datasets. Thus, using these five cases, we explore the usage of advanced machine learning and multivariate statistical techniques in the optimization of chemical and biochemical processes. The recent advances in computational hardware allow engineers to design such data-driven models, which enhances their capacity to effectively and efficiently monitor and control a process. We showcase that even non-expert chemical engineers can implement such machine learning algorithms with ease using open-source or commercially available software tools.
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    Simulation and Comparison of Operational Modes in Simulated Moving Bed Chromatography and Gas-Phase Adsorptive Separation
    Yu, Yueying (Virginia Tech, 2016-01-14)
    This dissertation describes the simulation and optimization of adsorptive and chromatographic separation processes. The first part focus on the simulation and comparison of operational modes in simulated moving bed (SMB) chromatography for separation and purification in bioprocesses. The second part includes the simulation of gas-phase adsorptive processes by pressure swing adsorption and temperature swing adsorption technologies. The applications of SMB chromatography are popular in separating and purifying enantiomers, petrochemicals, pharmaceuticals and biochemicals with higher yield and lower solvent consumption. We simulate and compare several operational modes of simulated moving bed (SMB) for a binary and a ternary bioprocess using Aspen Chromatography. These operational modes are able to improve the separation efficiency of the basic SMB process by our simulation and optimization. We compare their separation performances and identify heuristics that will guide the selection of operational modes across a variety of systems. Pressure swing adsorption (PSA) and temperature swing adsorption (TSA) are two of the main technologies for gas-phase adsorption separation processes. We simulate and demonstrate a PSA model for air separation system and a TSA model for CO2 capture system in Aspen Adsorption. We present their separation performance plots to provide the physical insights of these two systems.