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The detailed reaction kinetics and effect of reaction temperature on the performance of processes are fully analyzed.
The Life Cycle Assessment (LCA) methodology is widely applied in several industrial sectors to evaluate the environmental performance of processes, products and services.
Over time, the performance of processes may deviate from the initial design due to process variations and uncertainties, making it necessary to develop systematic methods for online optimality assessment based on routine operating process data.
A great way to derive evidence-based process improvement insights is to compare the behaviour and performance of processes for different process cohorts by utilising the information recorded in event logs.
Greening of SC has been identified as an important approach for improving the environmental performance of processes and products.
When quantifying the performance of processes and assessing the effects of improvement projects, performance indicators are an essential tool (Leyer et al. 2015).
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In this paper the Graphic Processing Unit (GPU) is applied in order to improve the computational performance of process systems optimal control calculations.
The utility of observability and redundancy in characterizing the performance of process data estimators was established in previous studies [10].
The second goal is to use the OHS parameters to benchmark the performance of process-based hydro-geochemical (HG) models.
Generally, it is challenging to evaluate the performance of process designs on the basis of a large number of indicators.
The objective of this work was to develop a model to evaluate the performance of process safety management systems of paint manufacturing facilities.
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