Machine-learning-based simulation and fed-batch control of cyanobacterial-phycocyanin production in Plectonema by artificial neural network and deep reinforcement learning-Reference-Cited by-同舟云学术

Machine-learning-based simulation and fed-batch control of cyanobacterial-phycocyanin production in Plectonema by artificial neural network and deep reinforcement learning

Published:2020-11 Issue: Volume:142 Page:107016
ISSN:0098-1354
Container-title:Computers & Chemical Engineering
language:en
Short-container-title:Computers & Chemical Engineering

Author:

Ma Yan,Noreña-Caro Daniel A.,Adams Alexandria J.,Brentzel Tyler B.,Romagnoli José A.,Benton Michael G.

Publisher

Elsevier BV

Subject

Computer Science Applications,General Chemical Engineering

Reference36 articles.

1. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems;Abadi,2015

2. State-of-the-art in artificial neural network applications: a survey;Abiodun;Heliyon,2018

3. Reinforcement learning–overview of recent progress and implications for process control;Badgwell,2018

4. Understanding dropout;Baldi,2013

5. Investigation of 2-nitrophenol solar degradation in the simultaneous presence of K2S2O8 and H2O2: using experimental design and artificial neural network;Bararpour;J. Clean. Prod.,2018

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