Applications of artificial intelligence and machine learning algorithms to crystallization
Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …
variety of scientific applications and cutting-edge technologies, where they have a …
Overview of surrogate modeling in chemical process engineering
K McBride, K Sundmacher - Chemie Ingenieur Technik, 2019 - Wiley Online Library
The ability to accurately model and simulate chemical processes has been paramount to the
growing success and efficiency in process design and operation. These improvements …
growing success and efficiency in process design and operation. These improvements …
Big data analytics in chemical engineering
Big data analytics is the journey to turn data into insights for more informed business and
operational decisions. As the chemical engineering community is collecting more data …
operational decisions. As the chemical engineering community is collecting more data …
NEXTorch: a design and Bayesian optimization toolkit for chemical sciences and engineering
Automation and optimization of chemical systems require well-informed decisions on what
experiments to run to reduce time, materials, and/or computations. Data-driven active …
experiments to run to reduce time, materials, and/or computations. Data-driven active …
Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques
SH Kim, F Boukouvala - Optimization Letters, 2020 - Springer
Optimization of simulation-based or data-driven systems is a challenging task, which has
attracted significant attention in the recent literature. A very efficient approach for optimizing …
attracted significant attention in the recent literature. A very efficient approach for optimizing …
Developing ANN-Kriging hybrid model based on process parameters for prediction of mean residence time distribution in twin-screw wet granulation
Artificial neural network (ANN) modelling is applied to predict the mean residence time of
pharmaceutical formulation in a twin-screw granulator. Process parameters including feed …
pharmaceutical formulation in a twin-screw granulator. Process parameters including feed …
Application of lignin in controlled release: development of predictive model based on artificial neural network for API release
Predictive models for simulation of drug release from tablets containing lignin as excipient
were developed in this work. Two predictive models including Artificial Neural Network …
were developed in this work. Two predictive models including Artificial Neural Network …
Process analysis and optimization of continuous pharmaceutical manufacturing using flowsheet models
Continuous manufacturing has attracted increasing research attention in the pharmaceutical
industry within the last decade. Based on the extensive experimental studies, numerous …
industry within the last decade. Based on the extensive experimental studies, numerous …
Investigate the effects of urban land use on PM2. 5 concentration: An application of deep learning simulation
L Zhao, M Zhang, S Cheng, Y Fang, S Wang… - Building and …, 2023 - Elsevier
As the fine particulate matter (PM 2.5) polluting seriously threat people's health, exploring its
mitigation strategies has become an urgent issue to be studied. Urban land use, the carrier …
mitigation strategies has become an urgent issue to be studied. Urban land use, the carrier …
[HTML][HTML] Identification of flow regime in a bubble column reactor with a combination of optical probe data and machine learning technique
ON Manjrekar, MP Dudukovic - Chemical Engineering Science: X, 2019 - Elsevier
In the present work, a data-driven model for identification of flow regime in a bubble column
is developed by combining data from optical probe technique and machine learning. Optical …
is developed by combining data from optical probe technique and machine learning. Optical …