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Full-Text Articles in Engineering
Machine Learning With Gradient-Based Optimization Of Nuclear Waste Vitrification With Uncertainties And Constraints, Lagrande Gunnell, Kyle Manwaring, Xiaonan Lu, Jacob Reynolds, John Vienna, John Hedengren
Machine Learning With Gradient-Based Optimization Of Nuclear Waste Vitrification With Uncertainties And Constraints, Lagrande Gunnell, Kyle Manwaring, Xiaonan Lu, Jacob Reynolds, John Vienna, John Hedengren
Faculty Publications
Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared …
Proactive Energy Optimization In Residential Buildings With Weather And Market Forecasts, Cody Simmons, Joshua Arment, Kody M. Powell, John Hedengren
Proactive Energy Optimization In Residential Buildings With Weather And Market Forecasts, Cody Simmons, Joshua Arment, Kody M. Powell, John Hedengren
Faculty Publications
This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. Heating, ventilation, and air conditioning …
Performance Comparison Of Low Temperature And Chemical Absorption Carbon Capture Processes In Response To Dynamic Electricity Demand And Price Profiles, Seyed Mostafa Safdarnejad, John Hedengren, Kody M. Powell
Performance Comparison Of Low Temperature And Chemical Absorption Carbon Capture Processes In Response To Dynamic Electricity Demand And Price Profiles, Seyed Mostafa Safdarnejad, John Hedengren, Kody M. Powell
Faculty Publications
Current projections to the year 2050 reveal that fossil fuels will remain the main source of energy generation. To achieve the target limits of carbon dioxide emission, set by national and international policies, carbon capture will play a key role. Modeling and optimization of various carbon capture technologies such as pre-combustion, oxy-fuel, and post-combustion, when integrated with coal-fired power plants, have been researched extensively in literature. Research on the integration of power generation with capture technologies regarding comparisons between the different schemes in response to dynamic inputs is lacking. This work provides a comparison between a low temperature carbon capture …
Gekko Optimization Suite, Logan Beal, Daniel Hill, Ronald Abraham Martin, John Hedengren
Gekko Optimization Suite, Logan Beal, Daniel Hill, Ronald Abraham Martin, John Hedengren
Faculty Publications
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and …
Optimal Combined Long-Term Facility Design And Short-Term Operational Strategy For Chp Capacity Investments, Jose Mojica, Damon Petersen, Brigham Hansen, Kody Powell, John Hedengren
Optimal Combined Long-Term Facility Design And Short-Term Operational Strategy For Chp Capacity Investments, Jose Mojica, Damon Petersen, Brigham Hansen, Kody Powell, John Hedengren
Faculty Publications
This work presents a detailed case study for the optimization of the expansion of a district energy system evaluating the investment decision timing, type of capacity expansion, and fine-scale operational modes. The study develops an optimization framework to find the investment schedule over 30 years with options of investing in traditional heating sources (boilers) or a next-generation combined heat and power (CHP) plant that provides heat and electricity. In district energy systems, the selected capacity and type of system is dependent on demand-side requirements, energy prices, and environmental costs. This work formulates capacity planning over a time horizon as a …
A Continuous Formulation For Logical Decisions In Differential Algebraic Systems Using Mathematical Programs Of Complementarity Constraints, Kody Powell, Ammon N. Eaton, John Hedengren, Thomas F. Edgar
A Continuous Formulation For Logical Decisions In Differential Algebraic Systems Using Mathematical Programs Of Complementarity Constraints, Kody Powell, Ammon N. Eaton, John Hedengren, Thomas F. Edgar
Faculty Publications
This work presents a methodology to represent logical decisions in differential algebraic equation simulation and constrained optimization problems using a set of continuous algebraic equations. The formulations may be used when state variables trigger a change in process dynamics, and introduces a pseudo-binary decision variable, which is continuous, but should only have valid solutions at values of either zero or one within a finite time horizon. This formulation enables dynamic optimization problems with logical disjunctions to be solved by simultaneous solution methods without using methods such as mixed integer programming. Several case studies are given to illustrate the value of …
Plant-Level Dynamic Optimization Of Cryogenic Carbon Capture With Conventional And Renewable Power Sources, Seyed M. Safdarnejad, John Hedengren, Larry Lin Baxter
Plant-Level Dynamic Optimization Of Cryogenic Carbon Capture With Conventional And Renewable Power Sources, Seyed M. Safdarnejad, John Hedengren, Larry Lin Baxter
Faculty Publications
Increasing competitiveness of renewable power sources due to tightening restrictions on CO2 emission from fossil fuel combustion is expected to cause a shift in power generation systems of the future. This investigation considers the impact of the Cryogenic Carbon Capture™ (CCC) process on transitional power generation. The CCC process consumes less energy than chemical and physical absorption processes and has an energy storage capability that shifts the parasitic loss of the CCC process away from peak hours. The CCC process responds rapidly to the variation of electricity demand and has a time constant that is consistent with the intermittent …
Nonlinear Modeling, Estimation And Predictive Control In Apmonitor, John Hedengren, Reza Asgharzadeh Shishavan, Kody M. Powell, Thomas F. Edgar
Nonlinear Modeling, Estimation And Predictive Control In Apmonitor, John Hedengren, Reza Asgharzadeh Shishavan, Kody M. Powell, Thomas F. Edgar
Faculty Publications
This paper describes nonlinear methods in model building, dynamic data reconciliation, and dynamic optimization that are inspired by researchers and motivated by industrial applications. A new formulation of the ℓ1-norm objective with a dead-band for estimation and control is presented. The dead-band in the objective is desirable for noise rejection, minimizing unnecessary parameter adjustments and movement of manipulated variables. As a motivating example, a small and well-known nonlinear multivariable level control problem is detailed that has a number of common characteristics to larger controllers seen in practice. The methods are also demonstrated on larger problems to reveal algorithmic …
Dynamic Optimization Of A Solar Thermal Energy Storage System Over A 24 Hour Period Using Weather Forecasts, Kody Powell, John Hedengren, Thomas F. Edgar
Dynamic Optimization Of A Solar Thermal Energy Storage System Over A 24 Hour Period Using Weather Forecasts, Kody Powell, John Hedengren, Thomas F. Edgar
Faculty Publications
A solar thermal power plant is used as a case study for dynamic heat integration with thermal energy storage. Findings show that thermal energy storage gives the system the ability to make the power dispatchable. Additionally, by solving a 24-hour dynamic optimization problem where the plant temperatures and power output are variable allows the system to capture and harvest a higher percentage of solar energy, with the most benefit occurring on mostly cloudy days. The solar energy captured increases 64% from 4.75 MWh to 7.80 MWh using this scheme. Hybrid plant operation and the ability to bypass the storage tanks …