Engineering topics

Immonen, Jake

Publications and source records attributed to Immonen, Jake.

7 records

DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗

DOE OSTI2023

Intelligent Control of Thermal Energy Storage in the Manufacturing Sector for Plant-Level Grid Response

Industrial facilities are seeking new strategies that help in providing savings mechanisms for demand charges. Demand charges are the charges incurred by industrial facilities as a result of power usage. Thermal energy storage has advanced significantly with lots of new applications, garnering the interest of many industrial facilities. These applications could be used to shave the industrial facilities’ peak electric demand and reduce their demand charges. This paper aims to demonstrate the efficacy of thermal energy storage in reducing demand charges and highlight new developments in the integration of smart control systems with thermal energy storage. The study compares energy consumption and peak demand for a facility equipped with and without thermal energy storage tanks using a fixed schedule for charging and discharging. Additionally, the paper examines the impact of incorporating a smart controller to determine when to charge and discharge the tank based on the facility’s real-time power usage and a given setpoint. The results indicate cost savings from the use of thermal energy storage tanks under two proposed scenarios, reflected in the reduced cost of power consumption for the studied facility. The incorporation of a smart controller with the thermal energy storage tank in the facility studied could provide estimated savings of 3.3% per year of power consumption charges, without considering the contribution of any incentives. The estimated savings provided by the fixed schedule scenario are 2.7% per year.

25 ENERGY STORAGE↗

DOE OSTI2022

Conversion of food waste to renewable energy: A techno-economic and environmental assessment

Increasing quantities of food waste have become a concern due to high disposal costs in landfills and high greenhouse gas emissions. With this increase in food waste generation, there is also an increasing demand for renewable natural gas to supplement traditional fossil fuel combustion and offset the impacts of climate change. Collecting food waste from landfills and turning it into renewable natural gas using anaerobic digestion could be a win-win option for both food waste disposal and renewable energy production. While some literature exists on the energy potential, economic feasibility, and environmental benefit of food waste disposal via anaerobic digestion, no existing study simultaneously evaluates the energy, economic and environmental effect of food waste to renewable energy via anaerobic digestion, especially on a plant and city scale. Further, this study is focused on the techno-economic and environmental assessment of food waste to energy via anaerobic digestion in order to fill this gap. Four anaerobic digestion pathways are considered in this study: flare, pipeline natural gas, combined heat and power, and combined cycle for efficient power generation. Using a city of 1M people the results show that renewable natural gas from food waste could supply the natural gas usage for 1.9% of residential use, 2.7% of commercial use, 1.1% of industrial use, 167.5% of the compressed natural gas vehicle fleet, 0.7% of electric power generation, or 2.5% of industrial high-temperature heating processes. All pathways except pipeline natural gas will have a positive net present value in the baseline scenario, and the pipeline natural gas pathway will become economically viable with a net present value of 31 USD/t of food waste with renewable energy credits. Lastly, all of the pathways achieve negative greenhouse gas emissions, which indicates that anaerobic digestion is a more environmentally friendly method for the handling of food waste than landfills.

03 NATURAL GAS↗

DOE OSTI2022

A review on the application of machine learning for combustion in power generation applications

Abstract Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.

Engineering↗

DOE OSTI2022

Dynamic optimization with flexible heat integration of a solar parabolic trough collector plant with thermal energy storage used for industrial process heat

There is an increasing need to reduce fossil fuel consumption used for industrial process heat to slow the effects of climate change. Using solar thermal heat is a viable way to replace fossil fuel use, but solar industrial process heat plants have limited implementation due to large upfront costs and inefficiencies from the inherent variability from solar energy. Having more flexible and optimized control over these plants can enable them to be more efficient. Here in this work, a solar industrial process heat plant with thermal energy storage that can flexibly collect and deliver heat to two industrial processes (flexible heat integration) is dynamically modeled with control setpoints found by a dynamic optimization method. The optimized case can increase the solar efficiency of the plant by 7.5% on average relative to a base case. The results show that it is best to collect heat at lower temperatures for all but ideal solar conditions, only medium to high quality heat should be stored in large quantities, and exchanging heat with a lower temperature heat sink is generally more efficient. The optimized case is able to reduce the levelized cost of heat of the plant to $31.83/MWh th compared to a base case value of $34.10/MWh th . The optimized case can reduce emissions by 22.2% compared to a plant which uses only natural gas. This work shows that dynamic optimization with flexible heat integration can be a cost-effective way to increase efficiency so that more of these types of plants can be implemented.

14 SOLAR ENERGY↗

DOE OSTI2022

Simulating a solar parabolic trough collector plant used for industrial process heat using an optimized operating scheme that utilizes flexible heat integration

This study focuses on the modeling and simulation of a novel design and operation of a solar industrial process heat (SIPH) plant that uses a parabolic trough collector system for generating process heat. The SIPH plant incorporates flexible heat integration (FHI) by having two options for heat sinks as well as a flexible collection temperature for the heat transfer fluid. Leveraging the degrees of freedom created by FHI allows an optimized operating scheme to be created which maximizes the solar heat delivered to the industrial processes. Yearly results for a 27 MW-t plant located in Salt Lake City, UT are simulated for a base case and a case utilizing optimized FHI. For the main process temperature case presented, FHI increases the total solar share by 13.0% and reduces the levelized cost of heat of the SIPH plant by 10.4% relative to the base case. Furthermore, three case study days are presented which highlight how FHI maximizes solar share, specifically during mornings, afternoons, and days with intermittent solar conditions. An environmental analysis is also performed showing that the SIPH plant can reduce harmful emissions by 15.4% for the base case and 17.4% for the FHI case compared to a plant that only utilizes a natural gas furnace for process heat. Overall, the work thoroughly explores an effective operating scheme for a SIPH plant, helping to make it more economical and environmentally beneficial.

14 SOLAR ENERGY↗

DOE OSTI2021

Techno-economic analysis and environmental benefits of solar industrial process heating based on parabolic trough collectors

In this work, detailed techno-economic and environmental analyses are conducted for employing a 5 MWt parabolic trough-based solar industrial process heat (SIPH) plant in Salt Lake City, Utah. According to the results, an optimum solar multiple of 1.5 was determined, allowing the plant to generate annual thermal energy of 15389.24 MWth with a capacity factor of 35.1% and levelized cost of heat (LCOH) of $\$ 26.3$/MWth. Considering a 30% investment tax credit and a 30% reduction in the total installed cost could reduce the LCOH to $\$ 19.30$/MWth and $\$ 18.52$/MWth, respectively. Employing parabolic trough collectors could avoid emissions of 3,582,422.47 kg, 147.99 kg, 3,341.66 kg, and 14.32 kg for CO 2 , PM, NOx, and SO 2 , respectively per year compared to a natural gas-based plant. Further, it also caused an annual external cost savings of between $\$ 99,900$ and $\$ 357,004$. Including the external costs in the LCOH analysis shows that the SIPH plant is economically competitive with a natural gas-based plant, further demonstrating its value. A further comparison demonstrated the economic superiority of a parabolic trough-driven IPH plant over a photovoltaic-driven electric boiler plant. In summary, with reducing installed costs and providing incentives, parabolic trough-driven IPH plants will be an economically and environmentally viable option.

14 SOLAR ENERGY↗