Optimizing the Controlling Parameters of a Biomass Boiler Based on Big Data

J He, J Zhang, L Wang, X Hu, J Xue, Y Zhao, X Wang… - Energies, 2023 - mdpi.com
This paper presents a comprehensive method for optimizing the controlling parameters of a
biomass boiler. The historical data are preprocessed and classified into different conditions …

Transformer neural networks with spatiotemporal attention for predictive control and optimization of industrial processes

ER Gallup, JF Tuttle, J Immonen… - 2024 American …, 2024 - ieeexplore.ieee.org
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 …

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

ER Gallup, J Tuttle, KM Powell - Computers & Chemical Engineering, 2024 - Elsevier
This study proposes using neural networks, specifically gated recurrent unit (GRU), long-
short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 …

Multi-source information fusion-based dynamic model for online prediction of rate of penetration (ROP) in drilling process

C Gan, X Wang, LZ Wang, WH Cao, KZ Liu… - Geoenergy Science and …, 2023 - Elsevier
Rate of penetration (ROP) accurate prediction can effectively reduce drilling cycle time and
operation cost. However, the complexity and variability of drilling process is not conducive to …

Utilization of an Advanced Sensor network to determine fuel heating value and Real-Time net unit heat rate during transient operation

K Stewart, C Moran, K Fowler, D McFarland, K Powell… - Fuel, 2024 - Elsevier
Coal-fired utility boilers are being increasingly used as variable electricity generation to
resolve the imbalance in the energy market from the expansion of intermittent renewable …

Dynamic hybrid modeling of LSTM-boosted mechanism and adversarial generation for industrial fuel ethanol fermentation process

X Li, X Yan - Journal of Process Control, 2023 - Elsevier
Fuel ethanol has great potential as a substitute for non-renewable energy. However,
modeling the pragmatic industrial process of fuel ethanol fermentation continues to be an …

[HTML][HTML] Meta-learning-based multi-objective PSO model for dynamic scheduling optimization

Z Liao, Y Liu, J Zhao - Energy Reports, 2023 - Elsevier
The by-product gas is an important secondary energy in the iron and steel industry. It is
important to make the by-product gas's utilization efficient and reasonable, which is the key …

[HTML][HTML] Dynamic adaptive control of boiler combustion based on improved GNG algorithm

W Wang, W Bai, Q Zhang, X Wang, S Dong… - Measurement …, 2024 - Elsevier
The boiler combustion process contains complex physicochemical changes, which is a
nonlinear time-varying industrial process with strong interference and multivariate strong …

Fair-AutoML: Enhancing fairness in machine learning predictions through automated machine learning and bias mitigation techniques

CR Komala, A Kumar, N Hema, S Nagarani… - AIP Conference …, 2024 - pubs.aip.org
The usage of machine learning (ML) in decision-making software is rising, but recent events
have cast doubt on the reliability of ML forecasts. To tackle this, new approaches and …

Evaluating Electrification of Fossil-Fuel-Fired Boilers for Decarbonization Using Discrete-Event Simulation

NI Chowdhury, B Gopalakrishnan, N Adhikari, H Li… - Energies, 2024 - mdpi.com
Decarbonizing fossil-fuel usage is crucial in mitigating the impacts of climate change. The
burning of fossil fuels in boilers during industrial process heating is one of the major sources …