Research on Chemical Process Optimization Based on Artificial Neural Network Algorithm

Artificial Neural Network (ANN) is established by imitating the human brain's nerve thinking mode. Because of its strong nonlinear mapping ability, fault tolerance and self-learning ability, it is widely used in many fields such as intelligent driving, signal processing, process control and so on. This article introduces the basic principles, development history and three common neural network types of artificial neural networks, BP neural network, RBF neural network and convolutional neural network, focusing on the research progress of the practical application of neural networks in chemical process optimization.

Implementing the ISO 15746 Standard for Chemical Process Optimization

This paper proposes an approach to integrating advanced process control solutions with optimization (APC-O) solutions, within any factory, to enable more efficient production processes. Currently, vendors who provide the software applications that implement control solutions are isolated and relatively independent. Each such solution is designed to implement a specific task such as control, simulation, and optimization — and only that task. It is not uncommon for vendors to use different mathematical formalisms and modeling tools that produce different data representations and formats. Moreover, instead of being modeled uniformly only once, the same knowledge is often modeled multiple times — each time using a different, specialized abstraction. As a result, it is extremely difficult to integrate optimization with advanced process control. We believe that a recent standard, International Organization for Standardization (ISO) 15746, describes a data model that can facilitate that integration. In this paper, we demonstrate a novel method of integrating advanced process control using ISO 15746 with numerical optimization. The demonstration is based on a chemical-process-optimization problem, which resides at level 2 of the International Society of Automation (ISA) 95 architecture. The inputs to that optimization problem, which are captured in the ISO 15746 data model, come in two forms: goals from level 3 and feedback from level 1. We map these inputs, using this data model, to a population of a meta-model of the optimization problem for a chemical process. Serialization of the metamodel population provides input to a numerical optimization code of the optimization problem. The results of this integrated process, which is automated, provide the solution to the originally selected, level 2 optimization problem.