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Deep hybrid modeling of chemical process: Application to hydraulic fracturing
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A mixed integer linear programming approach for the design of chemical process families
Tackling climate change goals requires widespread deployment of process technology variants across many decentralized sites with different geographical, environmental, and operational requirements. Conventional engineering approaches focus on unique designs for each installation (process variant), while missing opportunities for manufacturing standardization. Here, instead we seek to optimize a process platform of common unit designs while simultaneously designing an entire family of process variants that make use of that platform. This reduces engineering effort, deployment timelines, and manufacturing costs. We propose a nonlinear generalized disjunctive programming formulation and convert this to an efficient mixed-integer linear programming (MILP) formulation through discretization of the design space. We formulate our optimization in Pyomo with costing from IDAES, and we demonstrate the computational performance and solution quality on a water treatment desalination system from the PARETO framework and a carbon capture system built in Aspen Plus as part of CCSI2.
Mass-Constrained hybrid Gaussian radial basis neural networks: Development, training, and applications to modeling nonlinear dynamic noisy chemical processes
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The Influence of Nonlocal Metal–Dielectric Environments on Physical and Chemical Processes
Plasmonic nanolayers and laminar metallic/dielectric multilayers were originally developed for optical cloaking applications and lensing applications that could potentially image objects whose size was below the diffraction limit. These assemblies were initially formed from gold or silver nanorods grown within an alumina mesh. However, more recently, assemblies with similar properties have also been prepared by sequential thin-layer deposition of alternating layers of gold and magnesium fluoride (MgF 2 ). These metal/dielectric composite materials enable control of the dielectric constant in the directions perpendicular to the layers and balance the real and imaginary dielectric constants of the assembly such that the speed and the amplitude of the waves traveling through the assembly are not attenuated. In this Account, we will also focus on a few of the applications ranging from surface wetting to fluorescence quenching to enhancement of photochemical reactions. First, we will share an introduction to processes used to create these materials, which are combinations of low refractive index metals and transparent higher index materials arranged in a scalable repeating fashion. Two fabrication methods were employed: an electrochemical deposition of Ag nanorods into an anodized alumina matrix which produced materials with an anisotropic negative refractive index material within the plane of the film and lamellar metal/dielectric layers in which the negative index perpendicular to the growth direction. These alternating layers of plasmonic metals and dielectric materials were ultimately chosen to prepare films for further testing, because of their relative ease of fabrication. We will continue with a discussion of a few of the applications of both of these nonlocal dielectric composite materials including more specialized plasmonic, composite, and hyperbolic metamaterials including fluorescence quenching, photochemical reactions, and surface wetting. In each of these applications, the unique response caused by the enhancement of the electric field and the interface between hyperbolic materials and plasmonic materials as they interact photophysically with their near neighbors is presented. In each of the applications, the enhanced electric field extends from the composite substrate layer to interact with its near neighbors and beyond. Furthermore, the presence of this extended interaction can be observed in the form of decreased emission lifetime, enhancement of photochemical reaction rates, and changes in the surface energies measured by contact angle goniometry. In this Account, all of these situations will be addressed. Finally, we will conclude with a summary and vision for the future as well as a discussion of the unique challenges and opportunities available as research active faculty at an HBCU.
Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families
In previous work, we introduced process family design. The main idea is to design a platform of common elements, and, allowing us to capture additional cost savings, simultaneously design a family of processes, and reducing both engineering and deployment timelines. We formulate this as an optimization problem, specifically a nonlinear generalized disjunctive program (GDP). We have proposed two approaches for reformulating and solving this problem: one based on full-discretization of the design space and one that uses Machine Learning (ML) surrogates to replace the nonlinear process models. Using ML surrogates to predict required system costs and performance indicators allows us to reformulate the nonlinearities in the GDP generate an efficient MILP formulation. In this work, we apply the ML surrogate approach to two case studies. One case study involves designing a family of carbon capture systems to cover a set of different flue gas flow rates and inlet CO 2 concentrations, where we consider the absorber and stripper as common unit module types. The second case study focuses on a water-desalination process, where we design a family of these processes for a variety of salt concentrations and flow rates. In both of these case studies, we demonstrate a scalable optimization approach that enables the design of multiple processes simultaneously, reducing the time-to-market and overall costs by maximizing the cost savings due to both economies of scale and economies of numbers.
Plasma Assisted Combustion and Chemical Processing: Chapter 9 - Plasma Diagnostics
Plasma dynamics and chemistry have a broad range of timescales from picoseconds to milliseconds. In addition, it involves nonequilibrium energy transfer between electrons, ions, electronically and vibrationally excited states, radicals, intermediate species, and reactants and products as well as surface charges and chemistry. To understand plasma physics and chemistry, it is essential to conduct time and space resolved, quantitative detection of nonequilibrium temperature distributions, electron energy and number density, electric field, and species concentrations. There are enormous publications and review articles on this subject. The focus of this chapter is to be placed on the most recent progress in gas phase plasma properties and chemistry, especially on optical emission spectroscopy, laser absorption spectroscopy, Faraday rotational spectroscopy, Raman and Thompson scattering, femtosecond and picosecond (fs/ps) coherent anti-Stokes Raman scattering (CARS) spectroscopy, and electric field-induced second harmonic generation methods.
Chemical Processing Waste Remediation in the Plutonium-238 Supply Program
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FIU Project1: Chemical Process Alternatives for Radioactive Waste [Slides]
"The U.S. DOE Hanford Site has the largest number of high-level waste (HLW) storage tanks and the largest volume of HLW in the United States. The safe storage, retrieval, treatment, and disposal of approximately 53 million gallons of highly toxic, high-level radioactive waste stored in Hanford’s 177 underground tanks are a national priority. Retrieval and treatment of waste from these tanks pose a considerable challenge. Florida International University has been conducting research on several promising alternative processes and technologies that can be applied to address several technology gaps in the current waste retrieval and conditioning plans. Specifically, FIU has been involved in: the evaluation of non-metallic materials in the waste transfer system, evaluation of alternative HLW instrumentation for in-tank applications and the development of technologies to assist in the inspection of tank bottoms at Hanford."
Diagnostic Tools for Dynamic Models of Chemical Process Systems in Pyomo.DAE.
Abstract not provided.
Optimal Design Approaches for Rapid, Cost-Effective Manufacturing and Deployment of Chemical Processes.
Abstract not provided.
Optimal Design Approaches for Cost-Effective Manufacturing & Deployment of Chemical Process Families with Economies of Numbers
This work builds on our optimization formulation for process family design and extends it to explicitly include the benefits of economies of numbers. Economies of numbers (sometimes referred to as economies of learning) is a well-documented cost saving phenomenon. It characterizes the manufacturing cost savings due to standardization; in particular, it is capturing the correlation between cost reduction and the number of times a particular product has been manufactured. Following an approach similar to that in Gazzaneo et al. (2022), we develop a costing expression that captures material costs and manufacturing costs as a function of the number of unit modules produced. If the platform has a small number of unit module designs, we will be manufacturing a large number of each of these designs and gaining increased benefits from economies of numbers. However, increasing the number of unit module designs in the platform gives each process variant more choices to consider (at the cost of reducing economies of numbers). The optimization formulation in Stinchfield et al. (2023) pre-specified the number of unit module designs to be included in the platform. Here, by including the economies of numbers explicitly, we allow the mathematical programming formulation to determine the optimal number of unit module designs to include in the platform. We demonstrate this approach on multiple case studies, including MEA-based carbon capture and water desalination.
Develop a Simulation Framework for Understanding Physico-Chemical Processes and Optimization of CHZ' Plastic Thermolyze Technology (CRADA Final Report)
Thermolyzer(TM) technology is a third-generation, multi-reactor, oxygen-free, low pressure, slow pyrolysis process. It has proven to be remarkably versatile in successfully processing all types of hydrocarbon waste. These include all seven types of plastics, tires, auto shredder residue, carpet, electronic waste, and composites. Working with the IACMI, Thermolyzer(TM) technology has recovered the glass and carbon fibers from wind turbine blades for reuse into new applications. Its primary output is a synthesis gas that is rich in H 2 , CH 4 , and small amounts of C 2-4 aliphatic hydrocarbons. Other components are CO and CO 2 . Three unique features set Thermolyzer(TM) apart from other pyrolysis systems: the syngas is clean enough to run in Siemens or Solar gas turbines or IC engines without fouling, clean, salable, byproducts are produced, and CO 2 emissions are lower than natural gas power plants. Research results have been obtained from an operating 7 ton/day facility. A 44 ton/day plant has successfully operated as noted above. The process works because it makes use of the recoverable embodied energy in the feedstock. Surprisingly, a pound of some plastics has the same recoverable energy content as a pound of gasoline. Thus, it is imperative to develop a process that economically converts scrap plastics (including ocean plastics) into energy and thereby conserve the non-renewable fossil fuels for future generations. The Thermolyzer(TM) technology can also be used to create liquid fuels like gasoline and diesel from hydrocarbon wastes. Because of the high hydrogen content of the synthesis gas, hydrogen can be recovered more inexpensively than the current solar or wind energy being used to electrolyze water. That hydrogen can be used for fuel cell powered vehicles or converted into ammonia for agriculture or as a hydrogen storage medium. Extension of this technology to other wastes such as tires, auto shredder residue or wood wastes would expand the circular economy.
Optimal Design Approaches for Cost-Effective Manufacturing and Deployment of Chemical Process Families with Economies of Numbers
Developing methods for rapid, large-scale deployment of carbon capture systems is critical for meeting climate change goals. Optimization-based decisions can be employed at the design and manufacturing phases to minimize costs of deployment and operation. Manufacturing standardization results in significant cost savings due to economies of numbers. Building off previous work, we present a process family design approach to design a set of carbon capture systems while explicitly including economies of numbers savings within the formulation. Our formulation optimizes both the number and characteristics of the common components in the platform and simultaneously designs the resulting set of carbon capture systems. Savings from economies of numbers are explicitly included in the formulation to determine the number of components in the platform. We show and discuss the savings we gain from economies of numbers.
Chemical processes as methods of achieving high temperatures
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