ENBIS 2021 Spring Meeting

Session

Process optimization

1.1.1

17 May 2021, 10:00

Online

Conveners

Process optimization

• Marco P. Seabra dos Reis (University of Coimbra, Department of Chemical Engineering)

55. Are all data analytics techniques equally useful for process optimization in Industry 4.0?

Alberto J. Ferrer Riquelme (Universitat Politècnica de València)

17/05/2021, 10:00

Data Science in Process Industries

Machine learning techniques are becoming top trending in Industry 4.0. These models have been successfully applied for passive applications such as predictive modelling and maintenance, pattern recognition and classification, and process monitoring, fault detection and diagnosis. However, there is a dangerous tendency to use them indiscriminately, no matter the type of application. For...

56. Data driven modelling and optimisation of a batch reactor using bootstrap aggregated deep belief networks

Jie Zhang (Newcastle University)

17/05/2021, 10:20

Data Science in Process Industries

Batch reactors are suitable for the agile manufacturing of high value added products such as pharmaceuticals and specialty chemicals as the same reactors can be used to produce different products or different grades of products. Batch chemical reaction processes are typically highly nonlinear and batch to batch variations commonly exist in practice. Optimisation of batch process operation is...

67. Local batch time prediction based on the mixture of local batch experts: a case study on a polymerization process

Francisco Souza (Radboud University)

17/05/2021, 10:40

Data Science in Process Industries

Some batch processes have a large variability on the batch-to-batch time completion caused by process conditions and/or external factors. The local batch time is commonly inferred from process experts. However, this may lead to inaccuracies, due the uncertainty associated with the batch-to-batch variations, leading the process to run more than is really needed. Process engineers could appeal...