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Rethinking Computing Education with Vocareum and Canvas
Alexander Dowling
November 18, 2021 (v4)
Subject: Education
Keywords: Canvas, Colab, computer, data science, education, Jupyter, Learning Management System, Python, statistics, Vocareum
Presentation of Prof. Alexander Dowling's experience integrating Jupyter notebooks and computing into classes at the University of Notre Dame. Presented to ND faculty.
A 2-stage Approach to Parameter Estimation of Differential Equations using Neural ODEs
William Bradley, Fani Boukouvala
November 7, 2021 (v1)
Keywords: Neural ODEs, Neural-Networks, Nonlinear programming, parameter estimation
Modeling physio-chemical relationships using dynamic data is a common task in fields throughout science and engineering. A common step in developing generalizable, mechanistic models is to fit unmeasured parameters to measured data. However, fitting differential equation-based models can be computation intensive and uncertain due to the presence of nonlinearity, noise, and sparsity in the data, which in turn causes convergence to local minima and divergence issues. This work proposes a merger of Machine Learning (ML) and mechanistic approaches by employing ML models as a means to fit nonlinear mechanistic ODEs. Using a two-stage indirect approach, Neural ODEs are used to estimate state derivatives, which are then used to estimate the parameters of a more interpretable mechanistic ODE model. In addition to its computational efficiency, the proposed method demonstrates the ability of Neural ODEs to better estimate derivative information than interpolating methods based on algebraic... [more]
Perspectives on the Integration between First-Principles and Data-Driven Modeling
William Bradley, Jinhyeun Kim, Zachary Kilwein, Logan Blakely, Michael Eydenberg, Jordan Jalvin, Carl Laird, Fani Boukouvala
November 7, 2021 (v1)
Keywords: gaussian process regression, hybrid modeling, Machine Learning, model calibration, neural networks, physics-informed machine learning
Efficiently embedding and/or integrating mechanistic information within data-driven models is essentially the only approach to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive c... [more]
Supplemental Data for “Process Design and Techno-Economic Analysis of Biomass Pyrolysis By-Product Utilization in the Ontario and Aichi Steel Industries”
Jamie Rose, Thomas A. Adams II
November 5, 2021 (v1)
This is supplemental data for a paper submitted to the PSE 2021+ conference. It includes values used to calculate emissions reductions and financial value of biomass pyrolysis by-product utilization.
Valorization of Biomass Pyrolysis By-Products for Heat Production in the Ontario Steel Industry: A Techno-Economic Analysis
Jamie Rose, Thomas A. Adams II
November 5, 2021 (v1)
As part of efforts to reduce carbon emissions in the iron and steel industry, which are especially pertinent in Canada due to rising carbon taxes, Canadian producers have been investigating the effects of replacing coal used in pulverized coal injection with biochar. Although there has been research into the economic value and effect on net life cycle emissions of using the biochar product itself, there are no comprehensive techno-economic analyses which investigate the value and potential uses of the by-products of biomass pyrolysis. These by-products include volatile organic compounds, known collectively as tar or bio-oil, and light gases, which are mainly hydrogen, carbon monoxide, carbon dioxide, and methane. Since only 20-30% of the mass of pyrolyzed biomass is actually converted to char, with the rest converted to the by-products, [1] usage of these by-products is likely the key to increasing the value of biochar to a degree that makes up for the market price of biochar currently... [more]
Optimization under uncertainty of a hybrid waste tire and natural gas feedstock flexible polygeneration system using a decomposition algorithm
Avinash Subramanian, Rohit Kannan, Flemming Holtorf, Thomas A. Adams II, Truls Gundersen, Paul I. Barton
November 1, 2021 (v1)
Keywords: Decomposition Algorithm, Optimization under uncertainty, Polygeneration system, Stochastic Programming, Waste Tire, Waste-to-Energy
Market uncertainties motivate the development of flexible polygeneration systems that are able to adjust operating conditions to favor production of the most profitable product portfolio. However, this operational flexibility comes at the cost of higher capital expenditure. A scenario-based two-stage stochastic nonconvex Mixed-Integer Nonlinear Programming (MINLP) approach lends itself naturally to optimizing these trade-offs. This work studies the optimal design and operation under uncertainty of a hybrid feedstock flexible polygeneration system producing electricity, methanol, dimethyl ether, olefins or liquefied (synthetic) natural gas. The recently developed GOSSIP software framework is used for modeling the optimization problem as well as its efficient solution using the Nonconvex Generalized Benders Decomposition (NGBD) algorithm. Two different cases are studied: The first uses estimates of the means and variances of the uncertain parameters from historical data, whereas the seco... [more]