4.8 Review

Review on machine learning-based bioprocess optimization, monitoring, and control systems

Related references

Note: Only part of the references are listed.
Review Biotechnology & Applied Microbiology

Artificial intelligence and machine learning applications in biopharmaceutical manufacturing

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Summary: Artificial intelligence and machine learning have vast potential in optimizing the design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adopting these techniques include the increasing global demand for biotherapeutics and the rise of Industry 4.0, which require intelligent, automated supervision. This review summarizes the applications of artificial intelligence and machine learning in biopharmaceutical manufacturing, focusing on commonly used algorithms such as multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of these applications and the challenges in implementing them at manufacturing scale are also discussed.

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Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process

Ahmad Hosseinzadeh et al.

Summary: This study used machine learning (ML) procedures to model and analyze H-2 production from wastewater during dark fermentation, and found that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and AdaBoost were the most appropriate models. By optimizing the models with grid search and analyzing them deeply with permutation variable importance (PVI), the research identified the relative importance of process variables.

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Biofuels production from pine needles via pyrolysis: Process parameters modeling and optimization through combined RSM and ANN based approach

Shubhi Gupta et al.

Summary: The study aimed to assess the pyrolysis potential of pine needles and determined that a combined approach of response surface methodology and artificial neural network techniques showed better capability in modeling the process. The ANN model demonstrated higher R-2 values and lower MSE values, indicating its superiority in predicting process yield compared to RSM modeling. Temperature was identified as the most predominant variable influencing product yield, with optimized conditions predicting maximum bio-oil production.
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Prediction of biodiesel production from microalgal oil using Bayesian optimization algorithm-based machine learning approaches

Nahid Sultana et al.

Summary: This study successfully developed a model for biodiesel production using microalgae oil with Bayesian optimization algorithm-based machine learning techniques, which showed better performance compared to existing models. By hybridizing BOA with ANN and SVR, the model achieved higher accuracy and reliability, as validated by extra literature data.
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Harnessing the potential of machine learning for advancing Quality by Design in biomanufacturing

Ian Walsh et al.

Summary: Ensuring consistent high yields and product quality is a key challenge in biomanufacturing. Machine learning approaches have the potential to identify the impact of critical process parameters on product quality, enabling rational design and control of bioprocesses.
Review Agricultural Engineering

Recent advances in biofuel production through metabolic engineering

Swati Joshi et al.

Summary: The rising global energy demands and climate crisis have created an unprecedented need for the bio-based circular economy. Metabolic engineering is at the forefront of developing microbial chassis for biofuel bio-foundries to meet the industrial needs for clean energy.

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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor

Mohamad-Javad Mehrani et al.

Summary: A new method combining a mechanistic model and machine learning algorithms was developed to predict liquid N2O production during nitrification. The mechanistic model was used to simulate experimental trials, and the machine learning algorithms were used for prediction. Feature selection techniques were employed to identify the most relevant parameters for liquid N2O predictions. The proposed method enables fast and accurate prediction of liquid N2O concentrations with limited availability of measured data.

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Modeling nutrient removal by membrane bioreactor at a sewage treatment plant using machine learning models

Muhammad Yaqub et al.

Summary: This study developed machine learning models to predict nutrient removal in an anaerobic-anoxic-oxic membrane bioreactor. It was observed that considering all parameters significantly improved the predictive efficacy of the models. The XGBoost model showed the best performance in predicting NH4 removal.

JOURNAL OF WATER PROCESS ENGINEERING (2022)

Article Computer Science, Interdisciplinary Applications

AI-ML applications in bioprocessing: ML as an enabler of real time quality prediction in continuous manufacturing of mAbs

Saxena Nikita et al.

Summary: This paper explores the application of machine learning techniques for real-time prediction and process control of product quality in continuous manufacturing of biotherapeutics. By collecting and pre-processing data from sensors, tree-based regression techniques, particularly random forest models, achieve good performance in predicting critical process attributes.

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A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Pravin R. Kshirsagar et al.

Summary: This article discusses the harm of air pollution to humans and the environment, and introduces a method using machine learning algorithms to predict air pollution levels.

ELECTRONICS (2022)

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A Review on Machine Learning Application in Biodiesel Production Studies

Yuanzhi Xing et al.

Summary: Despite the exponential increase in fossil fuel consumption, biofuels are suggested as a more environmentally friendly alternative. Machine learning offers accurate and swift modeling for the intricate process of biodiesel production.

INTERNATIONAL JOURNAL OF CHEMICAL ENGINEERING (2021)

Article Agricultural Engineering

Production of biofuels from biomass: Predicting the energy employing artificial intelligence modelling

Manish Meena et al.

Summary: Bioenergy has the potential to replace fossil fuels and contribute to sustainable development while reducing dependency on traditional energy sources. Research shows that Artificial Intelligence technologies offer significant opportunities for improving bioenergy production systems and enhancing efficiency in energy prediction and optimization.

BIORESOURCE TECHNOLOGY (2021)

Article Green & Sustainable Science & Technology

Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives

Batsuren Sundui et al.

Summary: This study highlights the importance and advantages of using algae-bacteria consortia for wastewater treatment to achieve nutrient uptake and resource recovery. Machine learning algorithms play a crucial role in predicting uncertainties in the treatment process and provide reliable support for real-time monitoring, optimization, and fault detection in wastewater treatment systems.

CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY (2021)

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Integrated Model for Understanding N2O Emissions from Wastewater Treatment Plants: A Deep Learning Approach

Soonho Hwangbo et al.

Summary: This study demonstrates the application of deep learning in quantitatively describing long-term data from a Danish wastewater treatment plant, with a focus on N2O emissions. The study successfully develops a deep neural network for process modeling and identifies key parameters contributing to high N2O emissions through global sensitivity analysis. Additionally, the study shows that a LSTM-based forecasting model outperforms a DNN-based model in predicting N2O emissions.

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Review Agricultural Engineering

Biowaste hydrothermal carbonization for hydrochar valorization: Skeleton structure, conversion pathways and clean biofuel applications

Zhiming Zhang et al.

Summary: Hydrothermal carbonization (HTC) has the potential to convert wet biowaste into high-efficiency hydrochar resources, making it a renewable and sustainable way for biowaste recycling as biofuel.

BIORESOURCE TECHNOLOGY (2021)

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Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors

Mohamed Sherif Zaghloul et al.

Summary: Machine learning models were developed to simulate the AGS process using data collected from lab-based reactors for 475 days. Inputs were selected and model structure was optimized to successfully predict key parameters for treatment reactors.

WATER RESEARCH (2021)

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Recent advances in Bioprocess Technology-2020 Preface

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Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Rohan Gupta et al.

Summary: Drug designing and development faces challenges in terms of low efficacy, high cost, and complexity of data. However, artificial intelligence and machine learning technologies play a crucial role in overcoming these challenges and advancing the field.

MOLECULAR DIVERSITY (2021)

Review Biotechnology & Applied Microbiology

Machine learning for biochemical engineering: A review

Max Mowbray et al.

Summary: This article reviews the use of machine learning in biochemical engineering over the past 20 years, demystifying prevalent methods and outlining their impacts and obstacles in individual subfields, while also discussing core challenges and providing insight into the development of new digital biotechnologies through innovative modeling and learning strategies.

BIOCHEMICAL ENGINEERING JOURNAL (2021)

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Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae

Weijin Zhang et al.

Summary: Machine learning algorithms were applied to predict and optimize bio-oil production with algae compositions and HTL conditions as inputs. Gradient boosting regression showed better performance than random forest for prediction tasks. The importance of operating conditions was higher than algae characteristics for the three targets according to model-based interpretation.

BIORESOURCE TECHNOLOGY (2021)

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Modeling biohydrogen production using different data driven approaches

Yixiao Wang et al.

Summary: In this study, three modeling techniques were used to investigate the biohydrogen process. A new effective strategy for modeling and optimizing the complex BioH2 production during the dark fermentation was proposed. The proposed strategy is a useful and practical paradigm in modeling the BioH2 production process.

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Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance

Faramarz Bagherzadeh et al.

Summary: This study evaluated the effect of seven different Feature Selection methods on enhancing the prediction accuracy for total nitrogen in wastewater treatment plants. The results showed that scenario IV suggested by Mutual Information had the best performance. In addition, Gradient Boosting Machine demonstrated the best performance on unseen data-set, indicating its effectiveness for wastewater components prediction.

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AI Methods for Modeling the Vacuum Drying Characteristics of Chlorococcum infusionum for Algal Biofuel Production

Phoebe Mae L. Ching et al.

Summary: AI methods, including ANN and XGB, demonstrate higher accuracy in optimizing the efficiency of a vacuum drying process for algal-based biofuels. These models outperform traditional regression methods and show notable improvement in approximating individual sample points, especially at high and low tail-ends of the dataset. This study suggests potential for further optimization and automation based on the AI models developed.

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Hybrid-EKF: Hybrid model coupled with extended Kalman filter for real-time monitoring and control of mammalian cell culture

Harini Narayanan et al.

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Framework Based on Artificial Intelligence to Increase Industrial Bioethanol Production

Rauber D. Pereira et al.

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Biosystems Design by Machine Learning

Michael Jeffrey Y. Volk et al.

ACS SYNTHETIC BIOLOGY (2020)

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Hybrid modeling of cross-flow filtration: Predicting the flux evolution and duration of ultrafiltration processes

Maximilian Krippl et al.

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Machine learning exploration of the critical factors for CO2 adsorption capacity on porous carbon materials at different pressures

Xinzhe Zhu et al.

JOURNAL OF CLEANER PRODUCTION (2020)

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Comparison of adaptive neuro-fuzzy inference systems (ANFIS) and support vector regression (SVR) for data-driven modelling of aerobic granular sludge reactors

Mohamed Sherif Zaghloul et al.

JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING (2020)

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Exploiting machine learning for end-to-end drug discovery and development

Sean Ekins et al.

NATURE MATERIALS (2019)

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Data science tools and applications on the way to Pharma 4.0

Valentin Steinwandter et al.

DRUG DISCOVERY TODAY (2019)

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Marwin H. S. Segler et al.

ACS CENTRAL SCIENCE (2018)

Article Environmental Sciences

Machine learning for energy cost modelling in wastewater treatment plants

Dario Torregrossa et al.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2018)

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De Novo Design of Bioactive Small Molecules by Artificial Intelligence

Daniel Merk et al.

MOLECULAR INFORMATICS (2018)

Article Environmental Sciences

Machine Learning Algorithms for the Forecasting of Wastewater Quality Indicators

Francesco Granata et al.

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A multiple kernel learning algorithm for drug-target interaction prediction

Andre C. A. Nascimento et al.

BMC BIOINFORMATICS (2016)

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Online prediction of product titer and solubility of recombinant proteins in Escherichia coli fed-batch cultivations

Markus Luchner et al.

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Drug repositioning: a machine-learning approach through data integration

Francesco Napolitano et al.

JOURNAL OF CHEMINFORMATICS (2013)

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Artificial neural network modelling of a large-scale wastewater treatment plant operation

Dunyamin Guclu et al.

BIOPROCESS AND BIOSYSTEMS ENGINEERING (2010)

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Application of desirability function based on neural network for optimizing biohydrogen production process

Jianlong Wang et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2009)

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Quality by design for biopharmaceuticals

Anurag S. Rathore et al.

NATURE BIOTECHNOLOGY (2009)

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Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

Farouq S. Mjalli et al.

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Combining first principles modelling and artificial neural networks: a general framework

R Oliveira

COMPUTERS & CHEMICAL ENGINEERING (2004)

Article Biotechnology & Applied Microbiology

Optimization of a fermentation medium using neural networks and genetic algorithms

Y Nagata et al.

BIOTECHNOLOGY LETTERS (2003)

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Drug design by machine learning: support vector machines for pharmaceutical data analysis

R Burbidge et al.

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