According to Our World in Data, the industry sector contributes approximately 5.2 percent of the world's greenhouse gas emissions in 2016 [1]. Of that percentage, the cement industry contributes approximately 3 percent, thus accounting for more than 57 percent of all greenhouse gas emissions within the industry sector. Industrial-scale heating that is powered by renewable energy sources has the potential to combat this issue. This paper aims to analyze and model the Reverse Brayton Cycle to be used as a heat pump in a novel cement production system. The Simple Reverse Brayton Cycle and its potential concerning performance indicators such as coefficient of performance and scalability are determined. A Regenerative Brayton cycle is modeled in MATLAB® programming in order to be optimized and compared to conventional processes that require higher temperatures. Traditional manufacturing methods are discussed. Furthermore, possible methods of improvement are explored to view its effect on performance and temperatures between stages within the cycle.

Cyclical chemical looping involves the thermal reduction of metal oxide to release O2 at high temperatures, followed by its oxidation using O-containing molecules like O2, H2O, or CO2. This process is a promising method for solar thermochemical water splitting (STCH), oxygen separation, and thermochemical energy storage (TCES). The efficiency and economic viability of this process hinge on the thermodynamics of metal oxide reduction. This dissertation presents innovative methods to enhance the performance of these processes, with a specific focus on STCH and TCES by advancing thermodynamic characterization and screening of potential metal oxides, thereby reducing H2 costs.A novel CALPHAD approach, the CrossFit Compound Energy Formalism (CEF), integrates theoretical (density functional theory) and experimental (thermogravimetric) data to develop thermodynamic models for desired materials. The CrossFit-CEF was applied to BaxSr1-xFeO3-δ identifying matching thermodynamics and off-stoichiometric values to literature (~100-180 kJ/mol O2 reduction enthalpies across the BaxSr1-xFeO3-δ compositional range). Comparisons with the traditional van ‘t Hoff thermodynamic extraction technique reveal that the CrossFit-CEF method significantly outperforms conventional methods. For instance, the CEF method was employed to extract thermodynamic data for CaFexMn1-xO3-δ and identify optimal TCES CaFexMn1-xO3-δ compositions. The CrossFit-CEF method found the same thermodynamic trends on less than half the data utilized in a van ‘t Hoff approach and determined that the optimal CaFexMn1-xO3-δ composition had the minimal Fe concentration synthesized (x=0.625), achieving ~60 kJ/mol TCES. Bayesian Inference was employed was employed to expedite data collection. When combined with the CrossFit-CEF method, Bayesian Inference assesses the likelihood that the current model accurately describes the data, providing confidence intervals for the model. This approach reduces the amount of data needed for accurate thermodynamic modeling by 50%. Finally, the CrossFit-CEF and Bayesian methods are integrated into a system-level STCH model to optimize and accelerate materials design for specific plant operating conditions. Overall, this dissertation introduces methods that yield more accurate thermodynamic models with reduced data requirements. The time saved in data collection enables screening of more materials, expediting material identification and optimization. The materials identified through these techniques are expected to enhance chemical looping cycles, leading to increased H2 production efficiency and reduced costs.

This thesis presents the development of idiographic models (i.e., single subject or N = 1) of walking behavior as a means of facilitating the design of control systems to optimize mobile health (mHealth) interventions for sedentary adults. Model-on-Demand (MoD), an adaptive modeling technique, is demonstrated as an ideal method for modeling nonlinear systems with noise on a simulated continuously stirred tank reactor (CSTR). Comparing MoD to AutoRegressive with eXogenous input (ARX) estimation, MoD outperforms ARX in terms of addressing both nonlinearity and noise in the CSTR system. With the CSTR system as an initial proof of concept, MoD is then used to model individual walking behavior using intervention data from participants of HeartSteps, a walking intervention that studies the effect of within-day suggestions. Given the number of possible measured features from which to design the MoD models, as well as the number of model parameters that influence the model’s performance, optimizing MoD models through exhaustive search is infeasible. Consequently, a discrete implementation of simultaneous perturbation stochastic approximation (DSPSA) is shown to be an efficient algorithm to find optimal models of walking behavior. Combining MoD with DSPSA, models of walking behavior were developed using participant data from Just Walk, a day-to-day walking intervention; MoD outperformed ARX models on both estimation and validation data. DSPSA was also applied to ARX modeling, highlighting the use of DSPSA to not only search over model parameters and features but also data partitioning, as DSPSA was used to evaluate models under various combinations of estimation and validation data from a single participant’s walking data. Results of this thesis point to ARX with DSPSA as a routine means for dynamic model estimation in large-scale behavioral intervention settings.

Physical activity helps in reducing the risk of many chronic diseases, and plays a key role in maintaining good health of an individual. Just Walk is an intensively adaptive physical activity intervention, which has been designed based on system identification and control engineering principles. The goal of Just Walk is to design interventions that are responsive to an individual's changing needs, and thus encourage the individual to increase the number of steps walked.

Regularization is widely used in the field of machine learning. The goal of this thesis is to see how classical system identification principles in combination with machine learning methods like regularization help towards getting improved model estimates for complex systems. Estimating individual behavioral models using traditional prediction error methods can be done using an order selection. However, this method is can be computationally expensive due to the extensive search performed on a large set of order combination. If order selection is not done properly, it can cause bias (low order) and variance (high order) issues. In such cases regularization plays an important role in addressing the bias-variance trade-off.

One of the most important applications of identifying individual behavioral models is to understand what factors impact most the behavior of the person. Here "factors" can be considered as inputs (designed or environmental) to the participant over the course of the study, and the "behavior" is the step count of the participant under study. This is done by estimating models with different input combinations and then seeing which combinations of inputs (influence behavior most) give the best model estimate (best describe behavior of the person). As a part of this thesis, it is studied how regularized models can give a better estimation of personalized behavioral models, for the Just Walk study, which can further help in designing personalized interventions.

The study of subwavelength behavior of light and nanoscale lasing has broad

potential applications in various forms of computation i.e. optical and quantum, as well as

in energy engineering. Although this field has been under active research, there has been

little work done on describing the behaviors of threshold and saturation. Particularly, how

the gain-molecule behavior affects the lasing behavior has yet to be investigated.

In this work, the interaction of surface-plasmon-polaritons (SPPs) and molecules is

observed in lasing. Various phenomenologies are observed related to the appearance of the

threshold and saturation regions. The lasing profile, as a visual delimiter of lasing threshold

and saturation, is introduced and used to study various parametrical dependencies of lasing,

including the number-density of molecules, the molecular thickness and the frequency

detuning between the molecular transition frequency and the SPP resonant frequency. The

molecular population distributions are studied in terminal and dynamical methods and are

found to contain unexpected and theoretically challenging properties. Using an average

dynamical analysis, the simulated spontaneous emission cascade can be clearly seen.

Finally, theoretical derivations of simple 1D strands of dipoles are presented in both

the exact and mean-field approximation, within the density matrix formalism. Some

preliminary findings are presented, detailing the observed behaviors of some simple

systems.

The lack of healthy behaviors - such as physical activity and balanced diet - in

modern society is responsible for a large number of diseases and high mortality rates in

the world. Adaptive behavioral interventions have been suggested as a way to promote

sustained behavioral changes to address these issues. These adaptive interventions

can be modeled as closed-loop control systems, and thus applying control systems

engineering and system identification principles to behavioral settings might provide

a novel way of improving the quality of such interventions.

Good understanding of the dynamic processes involved in behavioral experiments

is a fundamental step in order to design such interventions with control systems ideas.

In the present work, two different behavioral experiments were analyzed under the

light of system identification principles and modelled as dynamic systems.

In the first study, data gathered over the course of four days served as the basis for

ARX modeling of the relationship between psychological constructs (negative affect

and self-efficacy) and the intensity of physical activity. The identified models suggest

that this behavioral process happens with self-regulation, and that the relationship

between negative affect and self-efficacy is represented by a second order underdamped

system with negative gain, while the relationship between self-efficacy and physical

activity level is an overdamped second order system with positive gain.

In the second study, which consisted of single-bouts of intense physical activity,

the relation between a more complex set of behavioral variables was identified as a

semi-physical model, with a theoretical set of system equations derived from behavioral

theory. With a prescribed set of physical activity intensities, it was found that less fit

participants were able to get higher increases in affective state, and that self-regulation

processes are also involved in the system.

Behavioral health problems such as physical inactivity are among the main causes of mortality around the world. Mobile and wireless health (mHealth) interventions offer the opportunity for applying control engineering concepts in behavioral change settings. Social Cognitive Theory (SCT) is among the most influential theories of health behavior and has been used as the conceptual basis of many behavioral interventions. This dissertation examines adaptive behavioral interventions for physical inactivity problems based on SCT using system identification and control engineering principles. First, a dynamical model of SCT using fluid analogies is developed. The model is used throughout the dissertation to evaluate system identification approaches and to develop control strategies based on Hybrid Model Predictive Control (HMPC). An initial system identification informative experiment is designed to obtain basic insights about the system. Based on the informative experimental results, a second optimized experiment is developed as the solution of a formal constrained optimization problem. The concept of Identification Test Monitoring (ITM) is developed for determining experimental duration and adjustments to the input signals in real time. ITM relies on deterministic signals, such as multisines, and uncertainty regions resulting from frequency domain transfer function estimation that is performed during experimental execution. ITM is motivated by practical considerations in behavioral interventions; however, a generalized approach is presented for broad-based multivariable application settings such as process control. Stopping criteria for the experimental test utilizing ITM are developed using both open-loop and robust control considerations.

A closed-loop intensively adaptive intervention for physical activity is proposed relying on a controller formulation based on HMPC. The discrete and logical features of HMPC naturally address the categorical nature of the intervention components that include behavioral goals and reward points. The intervention incorporates online controller reconfiguration to manage the transition between the behavioral initiation and maintenance training stages. Simulation results are presented to illustrate the performance of the system using a model for a hypothetical participant under realistic conditions that include uncertainty. The contributions of this dissertation can ultimately impact novel applications of cyberphysical system in medical applications.

Cigarette smoking remains a major global public health issue. This is partially due to the chronic and relapsing nature of tobacco use, which contributes to the approximately 90% quit attempt failure rate. The recent rise in mobile technologies has led to an increased ability to frequently measure smoking behaviors and related constructs over time, i.e., obtain intensive longitudinal data (ILD). Dynamical systems modeling and system identification methods from engineering offer a means to leverage ILD in order to better model dynamic smoking behaviors. In this dissertation, two sets of dynamical systems models are estimated using ILD from a smoking cessation clinical trial: one set describes cessation as a craving-mediated process; a second set was reverse-engineered and describes a psychological self-regulation process in which smoking activity regulates craving levels. The estimated expressions suggest that self-regulation more accurately describes cessation behavior change, and that the psychological self-regulator resembles a proportional-with-filter controller. In contrast to current clinical practice, adaptive smoking cessation interventions seek to personalize cessation treatment over time. An intervention of this nature generally reflects a control system with feedback and feedforward components, suggesting its design could benefit from a control systems engineering perspective. An adaptive intervention is designed in this dissertation in the form of a Hybrid Model Predictive Control (HMPC) decision algorithm. This algorithm assigns counseling, bupropion, and nicotine lozenges each day to promote tracking of target smoking and craving levels. Demonstrated through a diverse series of simulations, this HMPC-based intervention can aid a successful cessation attempt. Objective function weights and three-degree-of-freedom tuning parameters can be sensibly selected to achieve intervention performance goals despite strict clinical and operational constraints. Such tuning largely affects the rate at which peak bupropion and lozenge dosages are assigned; total post-quit smoking levels, craving offset, and other performance metrics are consequently affected. Overall, the interconnected nature of the smoking and craving controlled variables facilitate the controller's robust decision-making capabilities, even despite the presence of noise or plant-model mismatch. Altogether, this dissertation lays the conceptual and computational groundwork for future efforts to utilize engineering concepts to further study smoking behaviors and to optimize smoking cessation interventions.

Control engineering offers a systematic and efficient approach to optimizing the effectiveness of individually tailored treatment and prevention policies, also known as adaptive or ``just-in-time'' behavioral interventions. These types of interventions represent promising strategies for addressing many significant public health concerns. This dissertation explores the development of decision algorithms for adaptive sequential behavioral interventions using dynamical systems modeling, control engineering principles and formal optimization methods. A novel gestational weight gain (GWG) intervention involving multiple intervention components and featuring a pre-defined, clinically relevant set of sequence rules serves as an excellent example of a sequential behavioral intervention; it is examined in detail in this research.

 

A comprehensive dynamical systems model for the GWG behavioral interventions is developed, which demonstrates how to integrate a mechanistic energy balance model with dynamical formulations of behavioral models, such as the Theory of Planned Behavior and self-regulation. Self-regulation is further improved with different advanced controller formulations. These model-based controller approaches enable the user to have significant flexibility in describing a participant's self-regulatory behavior through the tuning of controller adjustable parameters. The dynamic simulation model demonstrates proof of concept for how self-regulation and adaptive interventions influence GWG, how intra-individual and inter-individual variability play a critical role in determining intervention outcomes, and the evaluation of decision rules.

 

Furthermore, a novel intervention decision paradigm using Hybrid Model Predictive Control framework is developed to generate sequential decision policies in the closed-loop. Clinical considerations are systematically taken into account through a user-specified dosage sequence table corresponding to the sequence rules, constraints enforcing the adjustment of one input at a time, and a switching time strategy accounting for the difference in frequency between intervention decision points and sampling intervals. Simulation studies illustrate the potential usefulness of the intervention framework.

The final part of the dissertation presents a model scheduling strategy relying on gain-scheduling to address nonlinearities in the model, and a cascade filter design for dual-rate control system is introduced to address scenarios with variable sampling rates. These extensions are important for addressing real-life scenarios in the GWG intervention.