A cluster-based competitive particle swarm optimizer with a sparse truncation operator for multi-objective optimization
Many different types of multi-objective optimization problems, eg multi-modal problems and
large-scale problems, have been solved with high performance by numbers of tailored multi …
large-scale problems, have been solved with high performance by numbers of tailored multi …
Multi-objective optimization of methanol production for energy efficiency and environmental sustainability
In this study, a syngas-to-methanol synthesis plant is modeled using Aspen Plus and
optimized using MATLAB-NSGA-II algorithm to simultaneously minimize total annual gas …
optimized using MATLAB-NSGA-II algorithm to simultaneously minimize total annual gas …
Fault detection and isolation using probabilistic wavelet neural operator auto-encoder with application to dynamic processes
Fault detection and isolation are crucial aspects that need to be considered for the safe and
reliable operation of process systems. The modern industrial process frequently employs …
reliable operation of process systems. The modern industrial process frequently employs …
Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders
Fault or anomaly detection is one of the key problems faced by the chemical process
industry for achieving safe and reliable operation. In this study, a novel methodology …
industry for achieving safe and reliable operation. In this study, a novel methodology …
[HTML][HTML] A graph embedding based fault detection framework for process systems with multi-variate time-series datasets
Due to the enormous potential of modelling, graph-based approaches have been used for
various applications in the process industries. In this study, we propose a fault detection …
various applications in the process industries. In this study, we propose a fault detection …
Optimisation of methanol distillation using GA and neural network hybrid
Distillation is an energy-intensive non-stationary process represented using non-linear
model equations and involves multiple objectives. For such processes, data-based multi …
model equations and involves multiple objectives. For such processes, data-based multi …
Scale abbreviation with recursive feature elimination and genetic algorithms: An illustration with the test emotions questionnaire
Psychological scales play a key role in the assessment, screening, and diagnosis of latent
variables, such as emotions, mental health, and well-being. In practice, researchers need …
variables, such as emotions, mental health, and well-being. In practice, researchers need …
Surrogate model-based optimization of methanol synthesis process for multiple objectives: A pathway towards achieving sustainable development goals
This work combines a generalized regression neural network (GRNN) with a non-dominated
sorting genetic algorithm (NSGA-II) to optimize a methanol synthesis plant for multiple …
sorting genetic algorithm (NSGA-II) to optimize a methanol synthesis plant for multiple …
Reconstruction error‐based fault detection of time series process data using generative adversarial auto‐encoders
Faults in time series process data are typically difficult to detect due to the complex temporal
correlations of data samples. In this context, traditional unsupervised machine learning …
correlations of data samples. In this context, traditional unsupervised machine learning …
Fault detection using Graph Neural Differential Auto-encoders (GNDAE)
In this study, we propose a Graph neural Differential Auto-encoder (GNDAE) model for fault
detection and process monitoring. The GNDAE framework is capable of dealing with graph …
detection and process monitoring. The GNDAE framework is capable of dealing with graph …