RAPID Funded Projects

This project represents a collaboration between the RAPID Module Manufacturing Focus Area (MMFA) and the Construction Industry Institute, within the Cockrell School of Engineering at the University of Texas at Austin. The research objective is to model the total cost of ownership (TCO) for scaling up via modular chemical process intensification (MCPI) and apply this model to four RAPID projects over the remaining course of the effort. This research is important for capturing the lessons learned within module manufacturing activities ongoing within the RAPID Institute and providing a rationale for numbering up via MCPI. Further, this research will help RAPID to apply a consistent means for quantifying the costs involved in MCPI as well as helping the MMFA understand cost drivers as impediments to MCPI adoption. The work plan involves a first year in which the model is developed including literature review and interviews with companies currently engaging in MCPI to identify opportunities for case study development. The second year will involve execution of formal data collection and analysis to understand the TCO for individual MCPI implementations.

One of the key technology gaps identified in the RAPID roadmap was to develop design tools and practices that would reduce the need for non-recurring engineering design costs in modular applications. This project is focused on developing integrated design and operating approaches for modular systems that can be deployed in the treatment of flowback and produced water resulting from shale gas production. Because of the highly distributed nature and variable characteristics of shale-gas wastewater (SGWW), there is a unique opportunity to deploy modular systems. There is also a major challenge in developing tailored designs for each source of wastewater. An integrated theoretical-experimental project is being executed to: (1) Assess, screen and integrate commercially-viable conventional and emerging technologies for wastewater treatment, (2) Develop computer-aided modeling, design, operation, scheduling, and costing approaches for non-recurring engineering needed to deploy the SGWW treatment systems, and (3) Demonstrate proof-of-concept via applications to a broad range of SGWW samples. A combination of systems engineering approaches and experimental/pilot-scale work will be used to generate commercially viable design and operational strategies with significant impact.

This project will demonstrate on-demand separation of multicomponent and multiphase water-oil mixtures using 3D-printed membranes. It is focused on wastewater treatment that is critical to the chemical industry. Application and adoption of intensified process design and 3D-printed membranes offers the prospect of revolutionizing the multicomponent and multiphase water-oil separation. While conventional membranes have been utilized in oil-water separation for some time, demonstration of 3D-printed membranes with well-controlled local structure, which renders the membrane to have multi-selectivity, is still lacking to-date. Moreover, wastewater treatment often involves many steps, and a more intensified process, which is enabled by a single multi-selectivity membrane, is highly desirable. The driving force for the proposed membrane is surface selectivity and topology rather than pressure and has been demonstrated in the laboratory. The present project aims to be a first-of-its-kind demonstration of the validity of the above-mentioned concept for the chemical industry.

This project looks to use IntraMicron’s platform technology of microfibrous entrapped catalysts (MFEC) to create a safer and more efficient process for the production of ethylene oxide (EO). Ethylene oxide is produced via the exothermic reaction of oxygen with ethylene. Because of the poor heat transfer and flow distribution in current packed bed reactors, hotspots form in the bed, resulting in poor selectivity. To mitigate these issues, EO processes are typically operated with sub-stoichiometric oxygen concentrations resulting in only a 10-12% ethylene conversion per pass. The use of thermal buffering inerts, such as CH4, and operating at low per pass conversion results in significant downstream costs associated with separations, recycle, BOP, and OPEX. This project aims to apply microfibrous entrapped catalyst (MFEC) with high thermal conductivity and inherent flame arresting propensity to safely increase single-pass conversion of current ethylene epoxidation processes. MFEC is a structured catalyst with an effective thermal conductivity 250 times higher than a typical packed bed. Because of its high thermal conductivity and highly porous nature, MFEC provides a near-isothermal intrabed temperature profile and reduces risks of hotspot formation, autoignition, and explosions.

RAPID aims to improve energy efficiency, reduce feedstock waste, and improve productivity by promoting modular chemical process intensification (PI) for processing industries in the U.S. manufacturing sector. To facilitate consistent and objective evaluation of performance metrics of various PI projects, RAPID has established this program to support and/or perform first principles-based process modeling for both baseline and intensified processes. Representing an alliance of academia, national laboratories, and industry, this project establishes a center for process modeling (CPM) responsible for process model-based metrics evaluation under RAPID sponsorship. The CPM objectives include: 1) to standardize and advance process modeling methodology for evaluating DOE performance metrics; 2) to validate and capture PI insights for RAPID PI projects with process models; and 3) to serve as the repository for RAPID process models for distribution, education, and continual refinement.

This project focuses on overcoming manufacturing and supply chain issues associated with a much needed modular technology solution in the gas processing sector. The team will look to take an existing technology for sour gas cleanup (processing scale on order of 1 T/day sulfur or 1 MMSCFD gas processed) and look to improve benefit vs. cost through pilot testing to improve performance and manufacturing design/analysis to determine highest leverage cost reduction steps. The resulting technology will be piloted in a field test to confirm economic assessments.

Almost every process in the chemical and processing industries (CPI) involves heat transfer. Integrated functioning of a variety of heat exchangers with gas, liquid, and vapor/liquid flows of single- and multi-component working fluids, is critical in any processing plant. Improving air and/or process-side performance can significantly reduce energy consumption and capital costs. This project is looking at the novel geometries and mechanical actuation to enhance heat exchanger performance. The level of improvement, and approach to modification will facilitate the design of both “bolt on” process enhancement for existing equipment and the design of standardized modular heat exchangers. Significant effort in this proposal will focus on taking leads demonstrated in the lab and looking to establish cost effective and scalable manufacturing approaches.

This project works to close the gap seen in the intensified process fundamentals area around how to enable modeling tools through the presentation of useful data for phenomena such as adsorption in complex systems. It looks to use meta-analysis of available databases to determine what data can currently be used with statistical confidence in its accuracy. Additionally, the project will also look to perform selected experiments to enhance this data set, and it will carry out simulations (validated by the data set that has been established) to further enhance the availability of a broad class of input data for process models.

Intensified processes are spatially and/or temporally coupled systems needing new modeling tools that go beyond systems analysis, and integrate reactor models with molecular scale models of chemical reactions. Current software at the quantum scale (density functional theory (DFT)) and the reactor scale (e.g., CFD) are widespread. In contrast, kinetics codes, especially for heterogeneous catalysis are at the proof-of-concept level due to outstanding technical barriers. This project will overcome these barriers by integrating existing software components and building missing ones from available prototypes. It will develop an open-source chemical kinetics software and data hub (OpenCK) as a transformative, cross cutting platform to address one of the most pressing gaps in process intensification (PI) and modular chemical process intensification (MCPI), namely the lack of a kinetics multiscale modeling software to plug and play (i.e., analyze, design, optimize, control), along with an associated hub of documented and validated models and data, a catalyst discovery ‘engine’, and toolkits for error analysis and assimilation of experimental data.

Advanced reactor designs with multiple catalysts are game-changers for process intensification. These reactors transform large, complex processes with multiple reactors to one-shot reactors, where complex reaction mechanisms can be exploited within a single unit. Such designs lead to layered and mixed catalyst beds that overcome equilibrium limitations, manage heat effects and improve product selectivity. These graded bed reactors have been considered for a number of reactive systems, ranging from Fischer-Tropsch synthesis, benzene hydrogenation, oxidative coupling of methane and steam reforming. This project develops and applies a new approach for the optimization of graded bed systems, based on EO-based optimization of fully discretized DAE (differential-algebraic equations) and PDAE (partial differential-algebraic equations) models. Known as direct transcription, this approach has been widely applied to challenging dynamic optimization problems, adapted to large-scale optimization software and is generally much faster and more reliable than with standard commercial tools. In particular, for graded beds, this approach stabilizes exponential forward modes and applies specialized regularization strategies in order to handle singular problem characteristics. As the target application, this project is especially devoted to improving the design and optimization methodologies for syngas to olefin (STO) processes, with emphasis on producing light (