Process Control and Systems Commons^™

A Data-Driven Multivariate Process Monitoring Platform For Knowledge Discovery And Model Building In Industrial Applications, Estelle E. Seghers

LSU Master's Theses

In industrial chemical manufacturing processes, the amount of raw data generated can add complexity in the analysis and understanding of the process dynamics. Being able to properly interpret this data can help improve plant operation, especially regarding safety and profitability. This research has culminated in FastMan-JMP, a platform proposed for monitoring of industrial processes and optimization of the offline data-driven model-building process as part of the process monitoring workflow. FastMan-JMP is a tool developed in Python to apply various data mining and machine learning techniques quickly and easily to better understand valuable patterns and hidden trends in process data. One …

Hybrid Modeling For Electrochemical Systems, Luis Alejandro Briceno-Mena

LSU Doctoral Dissertations

The discovery of new materials like catalysts, polymeric films, and biomolecules, is driven by industrial needs such as improving reaction or separation selectivity, enhancing therapeutic effects on medical treatments, or reducing costs of replacement. However, deployment of these advances in industrial applications is often hindered by the lack of models needed for design and optimization. Due to the novelty of materials and devices, experimental data and first principles' knowledge are scarce, making it hard to build models either via data-driven or knowledge based approaches. In this context, a way to efficiently combine domain knowledge with data could provide a pathway …

A Data-Based Framework For Monitoring And Controlling Particulate Systems, Vidhyadhar Manee

LSU Doctoral Dissertations

One of the limitations of conventional monitoring tools in crystallization is the inability to deal with high solids concentration. Image-based monitoring, in which RGB images of the solution are captured and analyzed by an object detection software, has been a promising alternative. The software used in these tools primarily depends on hand-coded heuristics to distinguish between the signal and the noise. With the recent success of supervised deep learning, a newer paradigm has emerged in which the heuristics can be learnt from labeled images. This approach is founded on the idea that it is easier to develop labels for data …

Model-Guided Design Of Rna-Based Synthetic Circuits For The Dynamic Regulation Of Gene Expression, Jordan R. Ryan

LSU Master's Theses

A longstanding goal in synthetic biology has been to build synthetic gene circuits with the ability to harness nature’s capability of precise gene expression regulation. Advancements in RNA technology have established RNA-based regulators with distinct advantages over traditional protein-based regulators such as faster signal propagation, versatile programmability, and low cellular burden, which has created an interest in the field to construct innovative synthetic gene circuits using de novo RNA- based regulators. However, our understanding of the behavior and kinetics of RNA-RNA interactions for the construction of gene circuits is incomplete. This thesis proposes a model-guided design framework that integrates mechanistic …

Machine Learning Based Applications For Data Visualization, Modeling, Control, And Optimization For Chemical And Biological Systems, Yan Ma

LSU Doctoral Dissertations

This dissertation report covers Yan Ma’s Ph.D. research with applicational studies of machine learning in manufacturing and biological systems. The research work mainly focuses on reaction modeling, optimization, and control using a deep learning-based approaches, and the work mainly concentrates on deep reinforcement learning (DRL). Yan Ma’s research also involves with data mining with bioinformatics. Large-scale data obtained in RNA-seq is analyzed using non-linear dimensionality reduction with Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), followed by clustering analysis using k-Means and Hierarchical Density-Based Spatial Clustering with Noise (HDBSCAN). This report focuses …

A Model-Based Framework For The Smart Manufacturing Of Polymers, Santiago D. Salas Ortiz

LSU Doctoral Dissertations

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems.

The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate …

A Comprehensive Techno-Economic Framework For Shale Gas Exploitation And Distribution In The United States, Jorge Asis Charbel Chebeir

LSU Doctoral Dissertations

Over the past years, shale gas has turned into one of the most significant sources of energy in the United States. Technological advancements have provided the energy industry with the necessary tools to allow the economic exploitation of an enormous volume of natural gas trapped in shale formations. This has boosted the domestic gas production and generated a boom in other sectors of the economy in the country. However, major challenges are involved in the development of shale gas resources. A drastic decline of wells’ productivity, the costs involved in the gas production and distribution facets, and the volatile behavior …