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In this paper, a Bayesian robust linear dynamic system approach is proposed for process modeling.
ANN, inspired by the structural and/or functional aspect of biological neural network, has attracted increasing attention in recent years, particularly for process modeling.
Hence, in the present study, multi-variant experimental design based on statistical approach such as response surface methodology (RSM) and artificial neural network (ANN) approach was applied for process modeling.
The paper is addressing the environmental protection and management concern by integrating different disciplinary components: environmental legislation aimed at preventing and reducing negative environmental impacts, engineering management aimed at designing the necessary systems for a coherent enterprises functioning, and IT as an efficiency tool for process modeling.
A literature review of advanced CCS technologies has been first carried out to gather information on technical inputs and parameters such as the component flow rates, temperature, pressure, and efficiencies for various units, required for process modeling.
Finally, some managerial insights for process modeling are given.
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The aim was to generate a data set more comprehensive than previously available, to support the development of an improved constitutive model for PET, suitable for process modelling.
Process Model is a standard for business process modeling that provides a graphical notation for specifying business processes in a Business Process Diagram (BPD).
Process modelling serves as a precondition for process model analysis.
However, that strategy may become unworkable for process models with many model inputs.
The objective is to generate HMTPs for process model development, applied in dryer design.
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