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Stochastic Optimization of Sustainable Industrial Symbiosis Based Hybrid Generation Bioethanol Supply Chains
(North Dakota State University, 2013) Gonela, Vinay
Bioethanol is becoming increasingly attractive for the reasons of energy security, diversity, and sustainability. As a result, the use of bioethanol for transportation purposes has been encouraged extensively. However, designing an effective bioethanol supply chain that is both sustainable and robust is still questionable. Therefore, this research focuses on designing a bioethanol supply chain that is: 1) sustainable in improving economic, environmental, social, and energy efficiency aspects; and 2) robust to uncertainties such as bioethanol price, bioethanol demand and biomass yield. In this research, we first propose a decision framework to design an optimal bioenergy-based industrial symbiosis (BBIS) under certain constraints. In BBIS, traditionally separate plants collocate in order to efficiently utilize resources, reduce wastes and increase profits for the entire BBIS and each player in the BBIS. The decision framework combines linear programming models and large scale mixed integer linear programming model to determine: 1) best possible combination of plants to form the BBIS, and 2) the optimal multi-product network of various materials in the BBIS, such that the bioethanol production cost is reduced. Secondly, a sustainable hybrid generation bioethanol supply chain (HGBSC), which consists of 1st generation and 2nd generation bioethanol production, is designed to improve economic benefits under environmental and social restrictions. In this study, an optimal HGBSC is designed where the new 2nd generation bioethanol supply chain is integrated with the existing 1st generation bioethanol supply chain under uncertainties such as bioethanol price, bioethanol demand and biomass yield. A stochastic mixed integer linear programming (SMILP) model is developed to design the optimal configuration of HGBSC under different sustainability standards. Finally, a sustainable industrial symbiosis based hybrid generation bioethanol supply chain (ISHGBSC) is designed that incorporates various industrial symbiosis (IS) configurations into HGBSC to improve economic, environmental, social, and energy efficiency aspects of sustainability under bioethanol price, bioethanol demand and biomass yield uncertainties. A SMILP model is proposed to design the optimal ISHGBSC and Sampling Average Approximation algorithm is used as the solution technology. Case studies of North Dakota are used as an application. The results provide managerial insights about the benefits of BBIS configurations within HGBSC.
Advanced Numerical Modeling in Manufacturing Processes
(North Dakota State University, 2022) Dey, Arup
In manufacturing applications, a large number of data can be collected by experimental studies and/or sensors. This collected data is vital to improving process efficiency, scheduling maintenance activities, and predicting target variables. This dissertation explores a wide range of numerical modeling techniques that use data for manufacturing applications. Ignorance of uncertainty and the physical principle of a system are shortcomings of the existing methods. Besides, different methods are proposed to overcome the shortcomings by incorporating uncertainty and physics-based knowledge. In the first part of this dissertation, artificial neural networks (ANNs) are applied to develop a functional relationship between input and target variables and process parameter optimization. The second part evaluates the robust response surface optimization (RRSO) to quantify different sources of uncertainty in numerical analysis. Additionally, a framework based on the Bayesian network (BN) approach is proposed to support decision-making. Due to various uncertainties, estimating interval and probability distribution are often more helpful than deterministic point value estimation. Thus, the Monte Carlo (MC) dropout-based interval prediction technique is explored in the third part of this dissertation. A conservative interval prediction technique for the linear and polynomial regression model is also developed using linear optimization. Applications of different data-driven methods in manufacturing are useful to analyze situations, gain insights, and make essential decisions. But, the prediction by data-driven methods may be physically inconsistent. Thus, in the fourth part of this dissertation, a physics-informed machine learning (PIML) technique is proposed to incorporate physics-based knowledge with collected data for improving prediction accuracy and generating physically consistent outcomes. Each numerical analysis section is presented with case studies that involve conventional or additive manufacturing applications. Based on various case studies carried out, it can be concluded that advanced numerical modeling methods are essential to be incorporated in manufacturing applications to gain advantages in the era of Industry 4.0 and Industry 5.0. Although the case study for the advanced numerical modeling proposed in this dissertation is only presented in manufacturing-related applications, the methods presented in this dissertation is not exhaustive to manufacturing application and can also be expanded to other data-driven engineering and system applications.
Transparent and Crack-Free Silica Aerogels
(North Dakota State University, 2012) Athmuri, Kalyan Ram
The process of making silica aerogels has been studied in detail over the past two decades due to its usage in a wide range of low end applications such as thermal insulators, supercapacitors etc., as well as high end applications like particle physics, space explorations. These applications call for control over the properties of aerogels, such as their transparency, density, porosity, pore size, and integrity. However, despite all the past research, controlling properties of aerogels is still not a fully developed science, a lot more research needs to be done. The literature on silica aerogels does not cover the study of the relation between transparency and cracks in aerogels – which can be a key factor in making aerogels for many applications. Hence, optimization of the transparency and integrity of the aerogels in order to obtain high transparency and low cracks was attempted in this thesis.
A Study on Deep Learning for Prognostics and Health Management Applications: An Evolutionary Convolutional Long Short-Term Memory Deep Neural Network Data-Driven Model for Prognostics of Aircraft Gas Turbine
(North Dakota State University, 2022) Khumprom, Phattara
The fundamental concept of prognostics and health management (PHM) within the scope of Condition-Based Maintenance (CBM) is to find an approach to evaluate the system health and predict its remaining useful life (RUL). Many methods and algorithms have been proposed for PHM modeling, most of which have been proven to perform relatively well. One of the leading algorithms in the current data-driven technology era is a deep learning approach, which is based on the concept of multiple hidden layers in a neural network. RUL prediction is an important part of PHM, which is the science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost and potential failure. The majority of the PHM models proposed during the past few years have shown a significant increase in the number systems that are data-driven. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible approach is to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods before the model training process. In this work, the effectiveness of multiple search-based methods that seek for the best features set to perform model training, which included, filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basic algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. It is believed that all of those approaches can also be applied to the prognostics of an aircraft engine. The aircraft engine data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods. The findings show that applying feature selection methods helps to improve overall model accuracy by 3% to 5% compared to other existing works and significantly reduces the complexity by using 7 out of 21 less input nodes for the deep learning type of models.
Stochastic Scheduling Optimization for Solving Patient No-show and Appointment Cancellation Problems
(North Dakota State University, 2015) Peng, Yidong
Patient non-attendance is the major challenge that reduces practice efficiency, resource utilization, and clinic accessibility, and leads to increased cost and diminished quality of care, while the clinic scheduling system is known as a determining factor for clinic efficiency, resource utilization and the accessibility of patients to healthcare facilities. A suitable and optimized scheduling system is one of the most important components for efficient care delivery to address the major challenges in the healthcare industry. Hence, reducing the adverse effect of patient no-shows and short-notice appointment notifications through the appointment scheduling approach is a strategically important matter for any healthcare systems. In this research, three patient scheduling models are proposed to address the patient non-attendance problem in the outpatient clinics. The first model is a two-stage mixed stochastic programming model, which can be used to optimize the overbooking decisions: (1) How many appointment slots should be overbooking; (2) Which appointment slot should be overbooking. In addition, this model also considers the cooperation between providers and patients’ choice. The second model is a Markov Decision Process (MDP) model, which can be used to optimize the walk-in patient admission policy in clinics with single physician by answering the four vital questions: (1) When the walk-in patient admission decisions should be made; (2) At each decision point, how many walk-in patients should be admitted; (3) Which provider should serve the admitted walk-in patients; (4) When the admitted walk-in patient should be served. By using this MDP model, heuristic optimal walk-in patient admission rules have been found for the single physician systems. For systems with more physicians, a more advanced two-stage mixed stochastic programming model (the third model) is proposed in order to make the optimal real time walk-in patient admission decisions. At last, it worthwhile to mention that novel solution approach has also been developed in order to solve these models in the efficient and effective manner.
Applying Simulation and Genetic Algorithm for Patient Appointment Scheduling Optimization
(North Dakota State University, 2013) Peng, Yidong
In this study, we discuss the implementation of integrated simulation and genetic algorithm for patient scheduling optimization under two different settings, namely the "traditional" scheduling system and the "open access" scheduling system. Under the "traditional" setting, we propose a two-phase approach for designing a weekly scheduling template for outpatient clinics providing multiple types of services. Our results demonstrate that the two-phase approach can efficiently find the promising weekly appointment scheduling templates for outpatient clinics. Under the "open access" setting, we propose a discrete event simulation and genetic algorithm (DES-GA) approach to find the heuristic optimal scheduling template for the clinic allowing both open access and walk-in patients. The solution provides scheduling templates consisting of not only the optimal number of reservations for open access appointments and walk-ins, but also the optimized allocation of these reserved slots, by minimizing the average cost per admission of open access or walk-in patient.
Evacuation Trees with Contraflow and Divergence Considerations
(North Dakota State University, 2018) Achrekar, Omkar Shirish
In this thesis, we investigate how to evacuate people using the available road transportation network efficiently. To successfully do that, we need to design evacuation model that is fast, safe, and seamless. We enable the first two criteria by developing a macroscopic, time-dynamic evacuation model that aims to maximize the number of people in relatively safer areas of the network at each time point; the third criterion is optimized by constructing an evacuation tree, where the vehicles are evacuated using a single path to safety. Divergence and contraflow policies have been incorporated to enhance the network capacity. Divergence enables specific nodes to diverge their flows into two or more streets, while contraflow allows certain streets to reverse their flow, effectively increasing their capacity. We investigate the performance of these policies in the evacuation networks obtained, and present results on two benchmark networks of Sioux Falls and Chicago.
Multi-Objective Optimal Phasor Measurement Units Placement in Power Systems
(North Dakota State University, 2014) Khiabani, Vahidhossein
The extensive development of power networks has increased the requirements for robust, reliable and secure monitoring and control techniques based on the concept of Wide Area Measurement System (WAMS). Phasor Measurement Units (PMUs) are key elements in WAMS based operations of power systems. Most existing algorithms consider the problem of optimal PMU placement where the main objective is to ensure observability. They consider cost and observability of buses ignoring the reliability aspect of both WAMS and PMUs. Given the twin and conflicting objectives of cost and reliability, this dissertation aims to model and solve a multi-objective optimization formulation that maintains full system observability with minimum cost while exceeding a pre-specified level of reliability of observability. No unique solution exists for these conflicting objectives, hence the model finds the best tradeoffs. Given that the reliability-based PMU placement model is Non-deterministic Polynomial time hard (NP-hard), the mathematical model can only address small problems. This research accomplishes the following: (a) modeling and solving the multi-objective PMU placement model for IEEE standard test systems and its observability, and (b) developing heuristic algorithms to increase the scalability of the model and solve large problems. In short, early consideration of the reliability of observability in the PMU placement problem provides a balanced approach which increases the reliability of the power system overall and reduces the cost of reliability. The findings are helpful to show and understand the effectiveness of the proposed models. However the increased cost associated with the increased reliability would be negligible when considering cost of blackouts to commerce, industry, and society as a whole.
Multiresponse Optimization Methodology Considering Related Quality Characteristics
(North Dakota State University, 2011) Thambidorai, Ganesh
Engineering problems often involve many conflicting quality characteristics that must be optimized simultaneously. Engineers are required to select suitable design parameter values which provide better trade-off among all quality characteristics. Multiresponse optimization is one of the most essential tools for solving engineering problems involving multiple quality characteristics. Optimizing several quality characteristics when the quality characteristics are correlated makes the optimization process more complex. The aim of this research is to evaluate the performance of several existing multiresponse optimization methods and investigate their capabilities in dealing with correlated quality characteristics. This study also investigates the impact of uncertainty in terms of input parameter selection. A new multi-response optimization approach has been proposed for solving correlated quality characteristics. The proposed approach is compared with the existing methods and found more robust in terms dealing with uncertainty in target selection. The comparative study and application of the proposed approach is demonstrated by considering two examples from the literature having correlated quality characteristics.
Study of Organizational Transformation from Socio-Technical Perspective
(North Dakota State University, 2020) Rahaman, Md Mahabubur
Organizations are constantly striving for effective and flexible means for managing challenges due to globalization and increasing customer expectations. Many in business community attempted to implement the Toyota Production System, or lean in their organizations to address the challenges. While the intent in many cases were to create a more flexible, effective and efficient organizations that meets the challenges of survival under external and internal pressures. However, the existing body of knowledge on lean is disperse and diverse in nature with respect to the application and implementation of lean tools and practices, making it difficult for researchers and practitioners to gain a real grasp of this topic. This research not only organizes the existing work on implementing lean but also documents challenges of implementation. The primary goal of this research is to study the organizational change and lean transformation from socio-technical perspective. In the process of discovery and empirical research, this work first, identifies challenges of organizational lean transformation. Second, it discovered organizational constructs from socio-technical perspective that has relevance on organizational challenge and lean transformation. Third, it proposed a hypothetical model, create a measurement model for predicting organizational change and lean transformation. Finally, this research tested a set of hypotheses. An Exploratory factor analysis (EFA) and subsequently a confirmatory analysis (CFA) was performed to identify the significance of latent organizational factors from socio-technical perspective as well as provide a theoretical model based on model fit indices exploiting path analysis (PA). This research contributed in providing a meaningful framework for organizational change and lean transformation and develop an instrument for measuring the organizational change and lean transformation for analyzing the gap or identify challenges in lean implementation from socio-technical perspective at organizational levels.

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