Process variable

About: Process variable is a research topic. Over the lifetime, 3983 publications have been published within this topic receiving 43130 citations. The topic is also known as: process parameter.

Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA

Abstract: In this study, Simulated Annealing (SA) and Genetic Algorithm (GA) soft computing techniques are integrated to estimate optimal process parameters that lead to a minimum value of machining performance. Two integration systems are proposed, labeled as integrated SA-GA-type1 and integrated SA-GA-type2. The approaches proposed in this study involve six modules, which are experimental data, regression modeling, SA optimization, GA optimization, integrated SA-GA-type1 optimization, and integrated SA-GA-type2 optimization. The objectives of the proposed integrated SA-GA-type1 and integrated SA-GA-type2 are to estimate the minimum value of the machining performance compared to the machining performance value of the experimental data and regression modeling, to estimate the optimal process parameters values that has to be within the range of the minimum and maximum process parameter values of experimental design, and to estimate the optimal solution of process parameters with a small number of iteration compared to the optimal solution of process parameters with SA and GA optimization. The process parameters and machining performance considered in this work deal with the real experimental data in the abrasive waterjet machining (AWJ) process. The results of this study showed that both of the proposed integration systems managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data.

Discontinuous optimization procedure modelling the run-idle status of plural process components

Abstract: A method of optimizing the operation of a process so that desired products are produced at minimum cost. The process has a plurality of process components, with each component having a run status and an idle status. The process has available more than one input and produces more than one output. The process is controlled by a computer which is provided with a mathematical model of the process, which model includes a model of each of its components. The model for each process component includes a logic variable, which logic variable can have only two values, 0 and 1, and which represent the run or idle status of each process component. The computer, when predetermined conditions arise, solves a mixed integer equation to determine the optimum state of the process at a given time to produce the desired outputs, at desired rates and at minimum cost; and, in doing so, determines the value of the logic variable for each component. When a change of status of a process component from run to idle or idle to run is required to place the process in its optimum state, control signals to cause such change of status of process components are applied to such components.

Modified independent component analysis and Bayesian network-based two-stage fault diagnosis of process operations

Abstract: Statistical fault detection techniques are able to detect fault and diagnose root-cause(s) from the monitored process variables. For complex process operations, it is not feasible to screen all the process variables due to monitoring cost and flooding of alarms. Thus, if a fault is originated from a process variable that is not monitored, conventional statistical techniques are incapable of locating the true root-cause. To relax this limitation, a two-stage fault diagnosis technique is proposed for process operations. In the first-stage, the modified independent component analysis is used for fault detection and to identify the faulty monitored variable. In the second-stage, a Bayesian Network model is constructed considering the process variables and their dependence obtained from the process flow diagram. Evidence is then generated at the network node corresponding to the faulty variable identified in the first-stage. Subsequently, the network is updated and analyzed using deductive and abductive reasoning to identify the true root-cause. To verify the applicability of the proposed technique it is tested on two process models. The results of both case studies have demonstrated the effectiveness of the proposed technique to diagnose the true root-cause that originated from process variables that are not monitored. Once integrated with process loss functions, the proposed technique will serve as an important element of dynamic operational risk management framework.

Polymerization process controller

Abstract: A batch polymerization process controller using inferential sensing to determine the integral reaction heat which in turn is used to indicate the degree of polymerization of the reaction mixture batch. The system uses a reaction temperature compared with a desired temperature, and the result is used as a feedback to monitor and control the process. One version of the process controller also uses a feedforward signal which is an integral reaction heat indication from a process model. In another version of the process controller, the integral reaction heat is compared with a desired integral reaction heat, and result is used as another feedback to monitor and control the process. Heat production and reaction temperature profiles may be used, along with the thermokinetic equations to determine the polymerization process and reactor models which are utilized by the process controller to optimize the polymerization process in terms of efficient use of cooling water and desired polymerization of each mixture batch.