A comparative study of applying active-set and interior point methods in MPC for controlling nonlinear pH process

A comparative study of Model Predictive Control (MPC) using active-set method and interior point methods is proposed as a control technique for highly non-linear pH process. The process is a strong acid-strong base system. A strong acid of hydrochloric acid (HCl) and a strong base of sodium hydroxide (NaOH) with the presence of buffer solution sodium bicarbonate (NaHCO3) are used in a neutralization process flowing into reactor. The non-linear pH neutralization model governed in this process is presented by multi-linear models. Performance of both controllers is studied by evaluating its ability of set-point tracking and disturbance-rejection. Besides, the optimization time is compared between these two methods; both MPC shows the similar performance with no overshoot, offset, and oscillation. However, the conventional active-set method gives a shorter control action time for small scale optimization problem compared to MPC using IPM method for pH control

Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass

In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO–ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R 2) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R 2 of the hybrid PSO–ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO–ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model

Inter-individual variability in propofol pharmacokinetic/pharmacodynamic (PK/PD) model – a sensitivity analysis

Inter-individual variability is a major challenge to guarantee adequate anaesthesia in patients across the population. This variability can occur as a result of patient physiology (e.g. age and weight), variations in the pharmacokinetic (PK) process and differences in the pharmacodynamic (PD) function. For a safe and effective drug administration, it is important to recognise which and when these factors of variability cause a higher uncertainty on depth of anaesthesia. This study aimed to quantify the influence of these input factors on the uncertainty in Bispectral Index (BIS). In this study, Sobol’ variance-based sensitivity analysis was performed on Schnider PK/PD model. Nine factors were evaluated: age, body weight, height, V1, V3, Cl1, Cl3, Ce50, and γ. The importance of these factors were ranked according to their total sensitivity indices. It was found that Ce50 has the most determining role on BIS prediction. γ is a significant factor during the induction phase. The PD model alone was found to responsible for 70% to 90% of BIS uncertainty during the maintenance phase. The variability of height has negligible influence on BIS prediction and can be omitted from the PK/PD model

Safety behavior and incident experience of worker in gas stations of Suratthani Province, Thailand

Transportation development in Thailand has grown at a rapid pace. LPG is a relatively popular automotive fuel in Thailand. The public have more interest in accident, prevention and reduce incidents in the workplace. Therefore, the aims of this research is to study safety behaviors, incident experience (IE) and investigate the safety behavior among worker who have never had incident experience (IE1) and worker who have had incident experiences (IE2) in gas stations. There were 76 respondents. We carried out an exploratory and descriptive study with respondents 19 LPG stations in Suratthani province, Thailand. The majority of workers have had incident experience in LPG stations. The biggest characteristic of these incidents were in process of filling LPG from the disperser to the customer’s car. There were leakage from the customer’s car and leakage from the equipment in the LPG station. The majority of consequences were the release of gas and collisions resulting in minor, major injury and other results. Besides that, the overall safety behavior of workers was very good. There were some behaviors where the level was moderate and poor. The results showed overall that (IE2) workers had better safety behavior than (IE1) workers. Hence, the companies have to maintain or promote good behaviors. Companies need to provide proper safety training, continually monitor and check to ensure good standards are maintained

Learning to Control pH Processes at Multiple Time Scales

This article presents a solution to pH control based on model-free learning control (MFLC). The MFLC technique is proposed because the algorithm gives a general solution for acid-base systems, yet is simple enough for implementation in existing control hardware. MFLC is based on reinforcement learning (RL), which is learning by direct interaction with the environment. The MFLC algorithm is model free and satisfying incremental control, input and output constraints. A novel solution of MFLC using multi-step actions (MSA) is presented: actions on multiple time scales consist of several identical primitive actions. This solves the problem of determining a suitable fixed time scale to select control actions so as to trade off accuracy in control against learning complexity. An application of MFLC to a pH process at laboratory scale is presented, showing that the proposed MFLC learns to control adequately the neutralization process, and maintain the process in the goal band. Also, the MFLC controller smoothly manipulates the control signal.Fil: Syafiie, S.. Universidad de Valladolid; EspañaFil: Tadeo, F.. Universidad de Valladolid; EspañaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin