(6ia) Building a New Computational Toolbox for Bioengineering and Advanced Manufacturing

Experience

2017-Pres., Johns Hopkins University and Princeton University (Postdoctoral Fellow/Visiting Researcher)

2016-2017, Delaware Biotechnology Institute, Postdoctoral Researcher

2012-2016, University of Delaware, Graduate Research Assistant

Keywords: complex and nonlinear systems, process control, data mining and machine learning, computational systems biology

Application areas: bioengineering and biomanufacturing, computer-interfaced biosystems, photovoltaics and semiconductor manufacturing

Process systems engineering has enabled routine high performance and high reliability in modern chemical manufacturing. Partly because of this success, the interests of chemical engineers have expanded to more fields than before, including biological systems, advanced materials, and renewable energy production and storage. Powerful new experimental tools have been developed for the design and analysis of such systems, including synchrotron methods, scattering and diffraction techniques, metabolic flux analysis, and a large suite of “-omics” techniques. Terabytes of data are produced, and analysis is not a human-scale problem any longer.

These experimental advances, coupled with new computational tools enabled by faster computer hardware and improved algorithms, motivate investigating new biological systems and advanced materials. Therefore, I will pursue two overall research thrusts:

• Computer-interfaced Biosystems
• Machine Learning Driven Material Design

For the first thrust, I plan to collaborate with expert synthetic biologists and metabolic engineers to develop new ways for integrating biological control systems with a computer in the loop. I will use the intrinsic ability of organisms and their native or engineered signaling pathways to sense and respond to stimuli in tandem with a human-designed hardware-driven control system. I expect that this approach will be useful for metabolic engineers seeking improved product yield, and for medical scientists seeking to understand and repair the control systems built-in to humans. For the second thrust, I plan to build models of important physical phenomena—such as thin film deposition kinetics or electrical performance of semiconductor devices—directly from data. These models will be valuable for understanding and designing new materials for many applications including energy storage and production. Here, I will review my current postdoctoral project, where I am developing tools for biomanufacturing using optogenetics, and my PhD project, where I designed a process for producing thin film solar cells.

1: Postdoctoral Project: Modeling and control of bioprocesses using optogenetics

Advised by Yannis G. Kevrekidis at Johns Hopkins University and José Avalos at Princeton University

Recent advances in synthetic biology enable a new biomanufacturing platform where high-resolution process inputs and outputs can be used for online optimization and control. In particular, biosensors and optogenetics allow for measuring and manipulating the state of a bioreactor with more precision than ever before. We are developing a “photobioreaction” process, where the reactor state is observed using biosensors and actuated with LEDs to manipulate optogenetic circuits. Abundant state information from biosensors can be used for data-assisted modeling and data-driven control system design. We are combining mechanistic models and new methods for dynamic system identification with tools from control systems theory to optimize photobioreactor performance. The effectiveness of these methods will be demonstrated through production of the advanced biofuel isobutanol. We are developing tools can be used for highly complex, multiple input, multiple output biological circuits that will be used for producing high-value products, including fuels, chemicals, and pharmaceuticals.

2. PhD Dissertation: Rapid thermal processing for production of chalcopyrite thin films for solar cells: Design, analysis, and experimental implementation

Advised by Babatunde A. Ogunnaike and Robert W. Birkmire at the University of Delaware

Due to their potential for low cost and high efficiency, thin film solar cells are a strong candidate for utility-scale electricity production. One attractive manufacturing route for thin film solar cells is rapid thermal processing (RTP). For my PhD research, I designed an RTP reactor for producing thin film solar cells and deployed a novel control system using an advanced model-based controller and a nonlinear observer. With the reactor in place, I developed a novel stochastic model to describe thin film deposition in the reacting system. I derived expressions to relate simulation parameters to physical properties, showed that the model could predict film composition—including composition gradients that affect solar cell performance—as observed in the lab, and used the stochastic nature of the model to characterize lateral heterogeneity in the film. I also showed that the model predictions correspond with experimental data. Finally, I applied statistical design of experiments to probe the effects of process variables on the material properties of the films and optimize the efficiency of solar cells.

Teaching Interests:

Experience

2016, University of Delaware, Assistant Instructor
Courses: Engineering Statistics (graduate), Chemical Engineering Laboratory (undergraduate)

2012, University of Delaware, Graduate Teaching Assistant
Course: Process Dynamics and Control (undergraduate)

I am dedicated to training future engineers and take seriously the responsibility of effective teaching. To ensure that I would become an effective educator, I sought out opportunities to build teaching experience throughout my academic career. I have experience as the instructor of computer labs (as a TA) and experimental labs (as an instructor), as well as lecturing experience at the graduate level. Beyond classroom experience, I helped develop an undergraduate laboratory project where students produced biodiesel from soybean oil using bench-scale and pilot-scale reactors, and presented this work at AIChE

Engineering lies in a unique position among most university departments. It demands technical proficiency in science and mathematics, yet also requires innovative problem-solving where core concepts are applied to new situations. I therefore plan to structure all my courses to include experiential learning, which is effective at helping students understand how fundamental theory can be applied. In experiential education, students first complete a realistic task (such as a laboratory experiment or a computer simulation) before being taught the underlying theory. Next, they are asked to reflect on the results, and, with guidance from the instructor, they can abstract and generalize the concepts. Finally, students are asked to apply the new concepts to new situations. I plan to use my experience in computer programming to build realistic computer simulations so that I can apply experiential education even in courses without a laboratory component.

Every class is composed of students with diverse experiences, learning styles, and academic preparation. To therefore ensure effective learning, I will develop and apply adaptive teaching methods, such that each class is structured flexibly and can accommodate all students. I will come to class prepared with different tools, including example problems, analogies, and visual demonstrations. In the classroom, instead of strictly adhering to a fixed syllabus, I will ensure that every learner in class has grasped the concepts before moving ahead. To achieve this, I will use example problems as a tool for class discussions, and make sure that everyone is participating.

I can confidently teach the entire chemical engineering undergraduate and graduate core curriculum: from introductory engineering through senior design and graduate-level kinetics, thermodynamics, and transport phenomena. Given my research background and teaching experience, I would choose to teach courses in process dynamics and control, engineering statistics, or other courses with a significant mathematical modeling component. For these courses, my research experience will allow me to draw examples from contemporary problems in semiconductor manufacturing, bioreactor control, and more, which will make the class more relevant and more interesting to students. Even for courses outside the domain of my research, however, my academic preparation has given me an appreciation for the importance of each course in the curriculum and I will include contemporary topics and current applications in the classroom.

Publications

2018, (under review), “Overcoming carrier concentration limits in polycrystalline CdTe thin films with in situ doping.” B. E. McCandless, W. A. Buchanan, C. P. Thompson, G. Sriramagiri, R. J. Lovelett, J. Duenow, D. Albin, S. Jensen, E. Colgrove, J. Moseley, H. Moutinho, S. Harvey, M. Al-Jassim, W. K. Metzger.

2017, AIChE Journal, “Hierarchical monitoring of industrial processes for fault detection, fault grade evaluation and fault diagnosis,” L. Luo, R. J. Lovelett, B. A. Ogunnaike. www.dx.doi.org/10.1002/aic.15662

2016, Journal of Process Control, “Design and experimental implementation of an effective temperature control system for thin film Cu(InGa)Se₂ production via rapid thermal processing,” R. J. Lovelett, G. M. Hanket, W. N. Shafarman, R. W. Birkmire, B. A. Ogunnaike. www.dx.doi.org/10.1016/j.jprocont.2016.07.005

2016, AIP Advances, “A stochastic model of solid state thin film deposition: Application to chalcopyrite growth,” R. J. Lovelett, X. Pang, T. M. Roberts, W. N. Shafarman, R. W. Birkmire, B. A. Ogunnaike. www.dx.doi.org/10.1063/1.4948404

2016, University of Delaware Ph.D. Dissertation “Rapid thermal processing for production of chalcopyrite thin films for solar cells: Design, analysis, and experimental implementation,” R. J. Lovelett, http://udspace.udel.edu/handle/19716/21450

2016, IEEE Photovoltaic Specialists Conference, “Growth of Cu(In,Ga)(S,Se)₂ films: Unravelling the mysteries by in-situ x-ray imaging,” B. West, M. Stuckelberger, L. Chen, R. J. Lovelett, B. Lai, J. Maser, W. Shafarman, M. Bertoni. www.dx.doi.org/10.1109/PVSC.2016.7749650

2015, IEEE Photovoltaic Specialists Conference, “A stochastic model for Cu(InGa)(SeS)₂ absorber growth during selenization/sulfization,” R. J. Lovelett, W. N. Shafarman, R. W. Birkmire, B. A. Ogunnaike. www.dx.doi.org/10.1109/PVSC.2015.7356226