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Process optimization is the discipline of adjusting a process so as to make the best or most effective use of some specified set of parameters without violating some constraint. Common goals are minimizing cost and maximizing throughput and/or efficiency. Process optimization is one of the major quantitative tools in industrial decision making.
PIDO stands for Process Integration and Design Optimization.Process Integration is needed as many software tools are used in a multi-domain system design. Control software is developed in a different toolchain than the mechanical properties of a system, where structural analysis is done using again some different tools.
Evolutionary Operation (EVOP) is a manufacturing process-optimization technique developed in the 1950s by George E. P. Box. [1] In EVOP, experimental designs and improvements are introduced, while an ongoing full-scale manufacturing process continues to produce satisfactory results. The idea is that process improvement should not interrupt ...
Process engineering activities can be divided into the following disciplines: [7] Process design: synthesis of energy recovery networks, synthesis of distillation systems (), synthesis of reactor networks, hierarchical decomposition flowsheets, superstructure optimization, design multiproduct batch plants, design of the production reactors for the production of plutonium, design of nuclear ...
Given a transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the output of the function and recording the best output values found during the process.
This process is called simulation optimization. [2] Specific simulation–based optimization methods can be chosen according to Figure 1 based on the decision variable types. [3] Fig.1 Classification of simulation based optimization according to variable types. Optimization exists in two main branches of operations research:
Process–architecture–optimization is a development model for central processing units (CPUs) that Intel adopted in 2016. Under this three-phase (three-year) model, every microprocessor die shrink is followed by a microarchitecture change and then by one or more optimizations.
Multi-task Bayesian optimization is a modern model-based approach that leverages the concept of knowledge transfer to speed up the automatic hyperparameter optimization process of machine learning algorithms. [8] The method builds a multi-task Gaussian process model on the data originating from different searches progressing in tandem. [9]