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Gunter, Daniel

Publications and source records attributed to Gunter, Daniel.

Model Diagnostics for Equation Oriented Models: Roadblocks and the Path Forward

This poster was presented at the Foundations of Computer Aided Process Design (FOCAPD) 2024 conference with a paper describing efforts to develop a unified workflow and toolbox for diagnosing issues in equation-oriented models as part of the IDAES modeling framework. The poster presents the work currently underway within the IDAES project to develop and integrate cutting edge model analysis techniques into a common toolbox and workflow for model developers to use.

Lee, Andrew↗

Model Diagnostics for Equation Oriented Models: Roadblocks and the Path Forward

Conference paper for FOCAPD24 describing efforts to develop a unified workflow and toolbox for diagnosing issues in equation-oriented models as part of the IDAES modeling framework. The paper presents the work currently underway within the IDAES project to develop and integrate cutting edge model analysis techniques into a common toolbox and workflow for model developers to use.

Lee, Andrew↗

OSU/NETL/Sandia/LBNL IDAES (CRADA Final Report)

The objective of this CRADA is to facilitate the cooperation among the Ohio State Univesity and the collaborators for the Institute for the Design of Advanced Energy Systems (IDAES). The goal is to develop, validate, and use a suite of process models based on the Ohio State's Fe-based coal direct chemical looping (CDCL) combustion process. This collaboration will provide Ohio State with a greater fundamental understanding of their chemical looping process, will help to improve the process efficiency, and will thus enable a greater probability of developing a commercial success. This collaboration will provide the IDAES team with detailed real-world process data to validate and demonstrate the effectiveness of its toolset.

01 COAL, LIGNITE, AND PEAT↗

Beyond Price Taker: Conceptual Design and Optimization of Integrated Energy Systems Using Machine Learning Market Surrogates

Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Design, Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) v0.1.0

The DISPATCHES software will enable the design and optimization of Integrated Energy Systems (IES). The platform will support hybridizing existing generation facilities (such as nuclear and coal plants) as well as greenfield hybrid designs that incorporate advanced technologies (such as Advanced Modular Reactors and Coal FIRST), and it will enable the consideration of multiple technology options with different time constants. The design of the IES will be optimized for operation within the bulk power system via energy market signals (day ahead, real-time, and ancillary services) obtained from a production cost model. The goal of DISPATCHES is to identify flexible, dynamic designs that are responsive to grid market signals resulting in highly efficient, cost effective tightly coupled hybrid energy systems that can act as a "black box" to the grid to provide increased flexibility and resiliency.

Gunter, Daniel↗

Pareto v1.0.0

An Optimization Framework for Produced Water Management and Beneficial Reuse

Beattie, KeithS↗

WaterTAP v1.0.0

WaterTAP is a new open source library of water-specific models built on the IDAES Integrated Platform to support the design and optimization of integrated water treatment systems, improve existing systems and enable the analysis of new designs incorporating emerging technologies. WaterTAP enables advanced modeling and optimization for: conceptual and multiscale design, dynamics and model predictive control of water systems and networks.

Beattie, KeithS↗

Abstract for CRADA among the National Energy Technology Laboratory, National Technology & Engineering Solutions of Sandia, The Regents of the University of California, and Tri-State Generation and Transmission Association, Inc. (AGMT-0855)

The objective of this CRADA is to facilitate cooperation among Tri-State Generation and Transmission Association, Inc. and collaborators of the Institute for the Design of Advanced Energy Systems (IDAES), including the National Energy Technology Laboratory, Sandia National Laboratories, and Lawrence Berkeley National Laboratory. The goal is to develop steady-state and dynamic optimization models of power plants. Tri-State will provide data related to the design and operation of power plants, which will be used to validate the IDAES team’s models and tools. These models will provide insight into potential improvements which could improve heat rate, reduce operations and maintenance costs, and/or improve reliability of the existing fleet of coal plants in the United States.

20 FOSSIL-FUELED POWER PLANTS↗

Abstract for CRADA between the National Energy Technology Laboratory, Ohio State University, Lawrence Berkeley National Laboratory, and Sandia National Laboratories (AGMT-0804)

This CRADA will enable cooperation between Ohio State University (OSU) and a collective led by the National Energy Technology Laboratory (NETL), with the goal of facilitating the accelerated commercialization of OSU’s iron-based chemical looping process through advanced modeling and state-of-the-art optimization techniques. OSU will provide data for model development and validation, while NETL and collaborators through the Institute for the Design of Advanced Energy Systems (IDAES) will develop advanced high-fidelity optimization-based system models. The project will use data collected primarily from OSU’s bench scale and sub pilot scale facilities and will be supplemented by data collected from the open literature. Models representative of chemical looping combustion or gasification using either coal or natural gas as fuel are considered in scope. Specific tasks will include 1) development of physical property sub-models, 2) development of steady-state and dynamic 1 st principles models of the novel gas solid contactor technologies being developed by OSU, and 3) superstructure and parametric optimization of a full chemical looping process by leveraging the large-scale optimization capabilities being developed by IDAES. This collaboration will provide a greater fundamental understanding of the OSU chemical looping process, will help to improve the overall process economics, and will thus enable a greater probability of commercial success. It will provide IDAES with the opportunity to validate, demonstrate, and further develop its toolset through a real-world practical demonstration.

01 COAL, LIGNITE, AND PEAT↗

Flowzilla: A Methodology for Detecting Data Transfer Anomalies in Research Networks

© 2018 IEEE. Research networks are designed to support high volume scientific data transfers that span multiple network links. Like any other network, research networks experience anomalies. Anomalies are deviations from profiles of normality in a research network's traffic levels. Diagnosing anomalies is critical both for network operators and users (e.g., scientists). In this paper we present Flowzilla, a general framework for detecting and quantifying anomalies on scientific data transfers of arbitrary size. Flowzilla incorporates Random Forest Regression(RFR) for predicting the size of data transfers and utilizes an adaptive threshold mechanism for detecting outliers. Our results demonstrate that our framework achieves up to 92.5% detection accuracy. Furthermore, we are able to predict data transfer sizes up to 10 weeks after training with accuracy above 90%.

Giannakou, Anna↗