RAPID Funded Projects

New York is the third largest dairy state in the U.S and it generates over 22 million tons of dairy and food wastes per year. Current waste management practices involve storage of untreated wastes in landfills and lagoons which pose significant environmental risks to river basins and lakes due to runoff and climate impacts resulting from fugitive methane emissions. Disposal and treatment of these wastes is typically viewed as a financial burden, but with the right combination of process technologies, it can become a resource for energy and nutrient recovery. The primary goal of this project is to evaluate the economic and technical feasibility of deploying a system of centralized biorefineries using a combination of Anaerobic Digestion (AD), Hydrothermal Liquefaction (HTL) and Biomethanation Power-to-gas (PtG) systems to process agricultural and food wastes. This project will specifically focus on spatial optimization and techno-economic modeling of these processes to develop a user-friendly assessment tool to highlight the potential of combining energy, dairy and food waste management systems to maximize resource recovery, reduce greenhouse gas emissions, and lowering local environmental impacts within a circular economy, all while ensuring cost-cutting and energy efficiency targets.

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 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 (

This project looks to achieve the aggressive goal of discovering potential MCPI process configurations that are both safe and operable based on using existing modeling approaches. The team will link together and expand upon existing modeling tools that are in various stages of development to create an environment that can define potential MCPI solutions without needing to define potential process schemes. This approach to process synthesis is high risk, but could create unanticipated and highly valuable solutions. As a test chemistry, the team will look at hydrogen production approaches to define potential MCPI solutions that improve upon SMR.