Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

Bikmukhametov, Timur; Jäschke, Johannes

doi:10.1016/j.compchemeng.2020.106834

Cited by 121 publications

(52 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Combining both first principles and machine learning algorithms can help to improve accuracy as well as the transparency of the VFM-based approaches, providing insight into the physical origins of the results [ 60 ]. For an in-depth review of the first principles and data-driven VFMs, we direct the reader to an excellent review by Bikmukhametov et al [ 42 ].…”

Section: Multiphase Fluid Flowmentioning

confidence: 99%

“…It means that a model generated by these algorithms is hard to interpret, and often the results give limited insight into the underlying physical processes. Therefore, several works [ 42 , 60 ] have tried to combine the physical features and first-principle methods with the black-box algorithms to ensure the reliability of the predictions, and facilitate the building of trust among stakeholders within the industry.…”

Section: Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Arief

Wiktorski

Thomas

2021

Sensors

View full text Add to dashboard Cite

Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.

show abstract

Section: Multiphase Fluid Flowmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Arief

Wiktorski

Thomas

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…combinations 42,43 . Often the hybrid models are also used to predict complex process phenomena, which is otherwise very difficult to mechanistically describe, e.g., cake formation on the cross-filtration unit or formation rates/kinetics of products in fermentation processes.…”

Section: The Rise Of Metagenomics Data and What To Do About Itmentioning

confidence: 99%

Activated sludge models at the crossroad of artificial intelligence—A perspective on advancing process modeling

Sin

2021

npj Clean Water

View full text Add to dashboard Cite

The introduction of Activated Sludge Models No. 1 (ASM1) in the early 1980s has led to a decade-long experience in applying these models and demonstrating their maturity for the wastewater treatment plants’ design and operation. However, these models have reached their limits concerning complexity and application accuracy. A case in point is that despite many extensions of the ASMs proposed to describe N2O production dynamics in the activated sludge plants, these models remain too complicated and yet to be validated. This perspective paper presents a new vision to advance process modeling by explicitly integrating the information about the microbial community as measured by molecular data in activated sludge models. In this new research area, we propose to harness the synergy between the rich molecular data from advanced gene sequencing technology with its integration through artificial intelligence with process engineering models. This is an interdisciplinary research area enabling the two separate disciplines, namely environmental biotechnology, to join forces and work together with the modeling and engineering community to perform new understanding and model-based engineering for sustainable WWTPs of the future.

show abstract

“…The history of production systems begins with the first community labor collectives. Having a primitive structure, constantly evolving together with the tasks that a person sets, having reached today a complex multi-network structure with thousands of elements [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

NBIC-convergence in production systems

et al. 2020

View full text Add to dashboard Cite

The paper reveals the current problems of production systems, their lack of readiness to function in the conditions of application and development of NBIC technologies. The methodological basis of the research consists of fundamental works and modern scientific and practical developments of national and foreign scientists on the problems of creation and functioning of production, technological and socio-economic systems in the conditions of development using NBIC technologies. The research was carried out using such general scientific and special methods as: historical-dialectical and abstract-logical; comparative-analytical and system analysis; synthesis of scientific approaches. The authors prove the need to create a new system paradigm. The new paradigm of production systems that realize the potential of NBIC technologies is based on a concept that finds its concrete expression, firstly, in the initial or original concepts, and secondly, in the system of basic principles, laws, or sufficiently plausible hypotheses. It is these elements that make up the conceptual core of the paradigm, and efforts should be directed to their search and justification when forming a new paradigm. The paper analyzes the main methods and tools for implementing a new approach to the design of production systems.

show abstract

Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

Cited by 121 publications

References 25 publications

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Activated sludge models at the crossroad of artificial intelligence—A perspective on advancing process modeling

NBIC-convergence in production systems

Contact Info

Product

Resources

About