Landen D. Blackburn

A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling

Jacob F. Tuttle, Landen D. Blackburn, Klas Andersson et al. · Applied Energy · 2021 · 75 citations · Full text

Dynamic Processes, Machine Learning, Data Science +10

Sustainable NO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e323" altimg="si4.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mi mathvariant="bold">x</mml:mi></mml:mrow></mml:msub></mml:math> emission reduction at a coal-fired power station through the use of online neural network modeling and particle swarm optimization

Jacob F. Tuttle, Ricky Vesel, Siva Alagarsamy et al. · Control Engineering Practice · 2019 · 72 citations

Math Xmlns, Mi Mathvariant=, Engineering +6

On-line classification of coal combustion quality using nonlinear SVM for improved neural network NOx emission rate prediction

Jacob F. Tuttle, Landen D. Blackburn, Kody M. Powell · Computers & Chemical Engineering · 2020 · 57 citations

Nonlinear Svm, Support Vector Machine, Coal Combustion Quality +9

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Landen D. Blackburn, Jacob F. Tuttle, Kody M. Powell · Journal of Cleaner Production · 2020 · 32 citations · Full text

Engineering, Machine Learning, Hybrid Algorithm +11

Development of novel dynamic machine learning-based optimization of a coal-fired power plant

Landen D. Blackburn, Jacob F. Tuttle, Klas Andersson et al. · Computers & Chemical Engineering · 2022 · 28 citations · Full text

Data-driven Optimization, Engineering, Machine Learning +7