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RESEARCH PAPER · AIChE Journal · 2022

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

Niket Sharma, Y. A. Liu

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A hybrid science‐guided machine learning approach for modeling chemical processes: A reviewOriginalParameter Identification Concept for Process Models Combining Systems Theory and Deep Learning3Process Model Inversion in the Data-Driven Engineering Context for Improved Parameter Sensitivities9Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies48Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing40Redundancy Analysis to Reduce the High-Dimensional Near-Infrared Spectral Information to Improve the Authentication of Olive Oil13Machine learning alternative to systems biology should not solely depend on data23Big data and machine learning driven bioprocessing – Recent trends and critical analysis56rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing21Hybrid modeling of hetero-agglomeration processes: a framework for model selection and arrangement15Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management103Machine learning methods for predicting the key metabolic parameters of Halomonas elongata DSM 2581 T4Enhancing the machine learning classification of olive oils by adding agro-climatic to NIR spectral information0Deep Learning Based System Identification and Nonlinear Model Predictive Control of pH Neutralization Process10A Hybrid Science‐Guided Machine Learning Approach for Modeling Chemical and Polymer Processes0Applications of Machine Learning to Optimizing Polyolefin Manufacturing0Intelligent Intercommunicating Multiscale Engineering: The Engineering of the Future8Bootstrap approach for quantifying the uncertainty in modeling of the water quality index using principal component analysis and artificial intelligence36Hamiltonian Neural Network 6-DoF Rigid-Body Dynamic Modeling Based on Energy Variation Estimation0Accelerating heat exchanger design by combining physics-informed deep learning and transfer learning36Adaptive Data‐Driven Modeling Strategy Based on Feature Selection for an Industrial Natural Gas Sweetening Process4Carbon‐negative syngas production: A comprehensive assessment of biomass pyrolysis coupling chemical looping reforming9A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems62The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering108Deep Neural Network Based Modelling of Chemisorption Process on Surface of Oxide Based Gas Sensors0Modeling Strategies Based on Multiple Neural Network Systems Applied for a Monopolar and Bipolar Electrocoagulation1Decentralized digital twins of complex dynamical systems23An efficient and invertible machine learning-driven multi-objective optimization architecture for light olefins separation system34A review and perspective on hybrid modeling methodologies160A Hybrid Predictive Modelling Approach for Catalyzed Polymerization Reactors0Overview of Challenges in Life Cycle Management of Digital Twins0Rethinking anaerobic digestion for bioenergy and biopolymers production: Challenges and opportunities1Hybrid data-driven and physics-based modeling for viscosity prediction of ionic liquids18Hybrid dynamic modeling of an industrial reactor network with first-principles and data-driven approaches14Design and optimization of an onboard boil-off gas re-liquefaction process under different weather-related scenarios with machine learning predictions11Challenges in data-based reactor modeling: A critical analysis of purely data-driven and hybrid models for a CSTR case study21Transparent AI-assisted chemical engineering process: Machine learning modeling and multi-objective optimization for integrating process data and molecular-level reaction mechanisms39Physics‐informed learning of chemical reactor systems using decoupling–coupling training framework16Hybrid residual modelling of biomass pyrolysis28Transparency: The Missing Link to Boosting AI Transformations in Chemical Engineering21Simulation of the physical temperature probes for soft sensor design under feed composition changes for naphtha plant3A hybrid approach combining mechanism-guided data augmentation and machine learning for biomass pyrolysis45Improving the accuracy of mechanistic models for dynamic batch distillation enabled by neural network: An industrial plant case4Bridging Data and AIOps for Future AI Advancements with Human-in-the-Loop. The AI-DAPT Concept1Deep learning model predictive control of a high-density polyethylene reactor with a physics-guided sequence-to-sequence model with memory6A deep learning-based approach for predicting coking duration in a commercial coke oven3Modelling a chemical plant using grey‐box models employing the support vector regression and artificial neural network1Reducing data requirement for accurate photovoltaic power prediction using hybrid machine learning-physical model on diverse dataset16Improved medical waste plasma gasification modelling based on implicit knowledge-guided interpretable machine learning11ROM-based stochastic optimization for a continuous manufacturing process1Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data7Bayesian Optimization of Low-Temperature Nonthermal Plasma Jet Sintering of Nanoinks9Integrated Machine Learning/FT-IR Framework for Efficient Solvent Composition Analysis in Carbon Capture6Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways17A physics‐constrained hybrid residual neural network for the prediction of moisture content in a closed‐cycle drying system4Agro-Climatic Information to Enhance the Machine-Learning Classification of Olive Oils from Near-Infrared Spectra3Optimizing olefin purification: An artificial intelligence-based process-conscious PI controller tuning for double dividing wall column distillation13Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises3A hybrid predictive modeling approach for catalyzed polymerization reactors4Physics-informed neural networks for dynamic process operations with limited physical knowledge and data58Artificial intelligence and machine learning at various stages and scales of process systems engineering36Methodology for hyperparameter tuning of deep neural networks for efficient and accurate molecular property prediction26Digital twins in process engineering: An overview on computational and numerical methods56Hybrid model using bond graph-TCN network and event triggered predictive control of pH neutralization process5Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation100Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach14Models, modeling and model-based systems in the era of computers, machine learning and AI31Combining CFD and AI/ML Modeling to Improve the Performance of Polypropylene Fluidized Bed Reactors14Applications of machine learning for modeling and advanced control of crystallization processes: Developments and perspectives24Machine learning assisted reactor and full process optimization design for alcohol oxidation10Bioeconomy of microalgae-based products0LAI retrieval from optical satellite imagery using residual physics-guided neural topologies with the PROSAIL RTM0LSTM-Based Hybrid Modeling Approach for Control Application of Evaporator Involving Phase Transition0Scientific Machine Learning for Industrial Process Simulation: Applications, Strategies, and Challenges0Knowledge Driven Machine Learning Towards Interpretable Intelligent Prognostics and Health Management: Review and Case Study48Review of physics-informed machine learning (PIML) methods applications in subsurface engineering16Integrating machine learning for multi-objective optimization of biomass conversion to hydrogen11A many-objective surrogate optimization model driven by hybrid pilot-test data, molecular reconstruction, and crude oil direct cracking reaction mechanism4A review on full-, zero-, and partial-knowledge based predictive models for industrial applications14Computer-aided many-objective optimization framework via deep learning surrogate models: Promoting carbon reduction in refining processes from a life cycle perspective7Magnetic Properties as Proxies for Geochemical Prediction in Mining Tailings: A Semi-Supervised Spatial Approach1Generalized engineering equations of heat-transfer performance for twisted heat exchanger with slurries from biogas plants by using Machine learning driven by mechanism and data2Estimating primary production in the California Current System using machine learning methods2Fault Diagnosis in Chemical Reactors with Data-Driven Methods13Hybrid modelling incorporating reaction and mechanistic data for accelerating the development of isooctanol oxidation2Real-time process safety and systems decision-making toward safe and smart chemical manufacturing25Development and assessment of hybrid machine learning model of biomass pyrolysis process18Rapid and accurate prediction of molecular dynamics simulations using physics-informed LSTM networks in engine emission analysis: A case study of C3H6/NH3 pyrolysis for PAH formation51A Framework for Assessing the Potential of Artificial Intelligence in the Circular Bioeconomy34Physics-based model as a versatile tool towards advanced process control of the naphtha distillation unit2Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review9Anchoring Computational Flow Models to Real-World Multiphase Reactors: Toward Ensuring Delivery of Materials and Energy at the Right Time and Place in Reactors5Explainable knowledge graph embeddings for industrial process monitoring & control7Interpretable and uncertainty-aware machine learning for trustworthy prediction in batch crystallization7Inverse design of the membrane reactors enabled by an inverse-forward physics-informed learning framework6Multi-scale revolution of artificial intelligence in chemical industry7A novel hybrid neural network for modeling dynamic systems using physics-informed regularization13Development of a Hybrid Model for the Paracetamol Batch Dissolution in Ethanol Using Universal Differential Equations0Building hybrid AI models in chemical engineering: A tutorial review17Design and optimization of shell-and-tube heat exchangers through ANN and H-ANN models7The Alchemist, the Scientist, and the Robot: Exploring the Potential of Human‐AI Symbiosis in Self‐Driving Polymer Laboratories8Perspective on artificial intelligence for carbon capture utilization and storage (CCUS) in Petrochemical Industry6Data-driven prediction of copper leaching yield from brass waste using stacking ensemble learning6A Review of Artificial Intelligence Applications for Biorefineries and Bioprocessing: From Data-Driven Processes to Optimization Strategies and Real-Time Control101A Multilinear Regression Procedure for Solving the Inverse Problem with Physics-Informed Neural Networks: Application to Three Case Studies in Chemical Engineering2Cement and concrete as carbon sinks: Transforming a climate challenge into a carbon storage opportunity3Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE’s representing nonlinear dynamic systems3AI Applications in the Environmental Control of Controlled Environment Agriculture in the Digital Age7Online parameter estimation and model maintenance using parameter-aware physics-informed neural network2Adding machine learning to the polymer reaction engineering toolbox13A practical method for maintaining the validity of hybrid AI process models without retraining1Scale-up of complex molecular reaction system by hybrid mechanistic modeling and deep transfer learning13Scientific machine learning for modeling industrial-scale Primary Separation Vessel1Multiscale modelling in ammonia synthesis: Integrating CFD and process simulation - A review3Machine learning accelerated data generation, process modelling and system optimization for biomass conversion and valorization16A Novel Hybrid Modeling Framework for Polymer Reaction Engineering with Application to Real-Time Process Monitoring0The incorporation of qualitative knowledge in hybrid modeling2Hybrid Modeling for Bioprocesses: Architectures, Applications, and Perspectives8Review of Applications of Experimental Designs in Wafer Manufacturing4A novel hybrid residual model for accurately predicting gas composition of biomass pyrolysis: From the perspective of biomass composition2Lyapunov-Constrained Hybrid Modeling for Stable Parameter Learning in Nonlinear Systems8Mechanistic, data-driven, and hybrid models: A critical comparison in surrogate drug dissolution modeling4Adaptive nickel–titanium shape memory alloy for smart systems: Mechanisms, manufacturing, and applications across biomedical, aerospace, civil, and energy8A hybrid approach for photobioreactor modeling in CO2 biofixation using microalgae cultivation3A Physics-Informed Grey-Box Predictor with an Online Bias Observer for Cargo Tank Pressure on In-Service LPG Carriers0Artificial Intelligence in Biomass Energy: Trends, Tools, and Technologies1Dual-level physics-guided modeling for efficient evaluation of energy-intensive drying processes0Hybrid Intelligence: The Fusion of Science-Based and Machine Learning Models0Machine Learning Augmented Mechanistic Modeling of Hydrogen-Based Reduction for Waste-Integrated Green Ironmaking0Data‐driven measuring and hybrid modeling of racemic methionine crystallization thermodynamics in K 2 CO 3 aqueous systems0Parallel Hybrid Modeling of Al–Mg–Si Tensile Properties Using Density-Based Weighting1End-to-end machine learning of Lyapunov-stable MPC for nonlinear systems with unknown dynamics0Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes7Physics-constrained machine learning for chemical engineering21Current state of the art and future perspectives for photocatalytic CO2 reduction14Design space determination of freezing processes for human induced pluripotent stem cell-derived spheroids using hybrid modeling0Exploring the Strengths and Limitations of Polymer Chemistry Informed Neural Networks1Interpretable machine learning for optimized dimethyl ether production from bio-methanol2Perspectives on the Essential Role of First-Principles Modeling in the Age of AI2Exploring the application boundaries of LLMs in mental health: a systematic scoping review13Adaptive physics-informed neural network-based digital twins integrated with Ensemble Kalman Filter6Empirical–theoretical Verilog-A modeling of nonlinear behavior in MEMS capacitive accelerometers0HyMech: AI-driven framework for physics-informed discovery of interpretable hybrid models1AI in chemical engineering: From promise to practice4A physics-guided adaptive soft sensor for real-time light olefin selectivity prediction in industrial MTO processes1Residual-hybrid dynamic response-surface/machine-learning framework for predicting malachite green removal and biodegradation by a bacterial consortium0Flow Modeling, Reactor Engineering, and Process Intensification: From Insight to Performance0Computational fluid dynamics and digital twins for wind turbines: A review3Experimental and Machine Learning Investigation of VLE/VLLE Behavior in the Methyl Acetate–Methanol System Using ChCl: EG Deep Eutectic Solvent1Physics-informed neural network-based multi-horizon model predictive control of chemical plants with renewable supply0Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns3

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