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Unfortunately, since multiple process units are not frequently built in the same region or using the same technologies, and because of confidentiality concerns in contract negotiation, significant constraints exist in data collection and the ability to provide reliable statistics on complexity factors.
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Given interaction data result in multiple assignments of genes to process units.
In order to aid the design of such facilities, this work presents a meta-heuristic optimisation approach using genetic algorithms where different levels of decision are addressed (facility, product sequence and unit operation) and multiple process and business criteria are used to evaluate each alternative generated.
Process units are assumed independent.
In the latter case, either the interplay of multiple processes is responsible for the stabilized unit, or the superposition of several independent but distinct processes.
Multiple processes are monitored.
Some approaches also consider multiple processes.
Multiple processes contribute to these relationships.
The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice.
The data sets on LRVs of human viruses and bacteriophages are scarce except for MBR and conventional activated sludge processes, which highlights the necessity of investigating LRVs of human viruses and bacteriophages in multiple treatment unit processes.
This algorithm has high computational cost and therefore needs to be processed on multiple computing units, making it necessary to use four FPGAs of the GiDEL platform in this study case.
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