Optimal predictive control of water transport systems: Arrêt-Darré/Arros case study

This paper proposes the use of predictive optimal control as a suitable methodology to manage efficiently transport water networks. The predictive optimal controller is implemented using MPC control techniques. The Arrêt-Darré/Arros dam-river system located in the Southwest region of France is proposed as case study. A high-fidelity dynamic simulator based on the full Saint-Venant equations and able to reproduce this system is developed in MATLAB/SIMULINK to validate the performance of the developed predictive optimal control system. The control objective in the Arrêt-Darré/Arros dam-river system is to guarantee an ecological flow rate at a control point downstream of the Arrêt-Darré dam by controlling the outflow of this dam in spite of the unmeasured disturbances introduced by rainfalls incomings and farmer withdrawals

The influences of basic physical properties of clayey silt and silty sand on its laboratory electrical resistivity value in loose and dense conditions

Non-destructive test which refers to electrical resistivity method is recently popular in engineering, environmental, archaeological and mining studies. Based on the previous studies, the results on electrical resistivity interpretation were often debated due to lack of clarification and evidences in quantitative perspective. Traditionally, most of the previous result interpretations were depending on qualitative point of view which is risky to produce unreliable outcomes. In order to minimise those problems, this study has performed a laboratory experiment on soil box electrical resistivity test which was supported by an additional basic physical properties of soil test like particle size distribution test (d), moisture content test (w), density test (ρbulk) and Atterberg limit test (LL, PL and PI). The test was performed to establish a series of electrical resistivity value (ERV) with different quantity of water content for clayey silt and silty sand in loose and dense condition. Apparently, the soil resistivity value was different under loose (L) and dense (C) conditions with moisture content and density variations (silty SAND = ERVLoose: 600 - 7300 Ωm & ERVDense: 490 - 7900 Ωm while Clayey SILT = ERVLoose: 13 - 7700 Ωm & ERVDense: 14 - 8400 Ωm) due to several factors. Moreover, correlation of moisture content (w) and density (ρbulk) due to the ERV was established as follows; Silty SAND: w(L) = 638.8ρ-0.418, w(D) = 1397.1ρ-0.574, ρBulk(L) = 2.6188e-6E-05ρ, ρBulk(D) = 4.099ρ-0.07 while Clayey SILT: w(L) = 109.98ρ-0.268, w(D) = 121.88ρ-0.363, ρBulk(L) = -0.111ln(ρ) + 1.7605, ρBulk(D) = 2.5991ρ-0.037 with determination coefficients, R2 that varied from 0.5643 – 0.8927. This study was successfully demonstrated that the consistency of ERV was greatly influenced by the variation of soil basic physical properties (d, w, ρBulk, LL, PL and PI). Finally, the reliability of the ERV result interpretation can be enhanced due to its ability to produce a meaningful outcome based on supported data from basic geotechnical properties

The influences of basic physical properties of clayey silt and silty sand on its laboratory electrical resistivity value in loose and dense conditions

Non-destructive test which refers to electrical resistivity method is recently popular in engineering, environmental, archaeological and mining studies. Based on the previous studies, the results on electrical resistivity interpretation were often debated due to lack of clarification and evidences in quantitative perspective. Traditionally, most of the previous result interpretations were depending on qualitative point of view which is risky to produce unreliable outcomes. In order to minimise those problems, this study has performed a laboratory experiment on soil box electrical resistivity test which was supported by an additional basic physical properties of soil test like particle size distribution test (d), moisture content test (w), density test (ρbulk) and Atterberg limit test (LL, PL and PI). The test was performed to establish a series of electrical resistivity value (ERV) with different quantity of water content for clayey silt and silty sand in loose and dense condition. Apparently, the soil resistivity value was different under loose (L) and dense (C) conditions with moisture content and density variations (silty SAND = ERVLoose: 600 - 7300 Ωm & ERVDense: 490 - 7900 Ωm while Clayey SILT = ERVLoose: 13 - 7700 Ωm & ERVDense: 14 - 8400 Ωm) due to several factors. Moreover, correlation of moisture content (w) and density (ρbulk) due to the ERV was established as follows; Silty SAND: w(L) = 638.8ρ-0.418, w(D) = 1397.1ρ-0.574, ρBulk(L) = 2.6188e-6E-05ρ, ρBulk(D) = 4.099ρ-0.07 while Clayey SILT: w(L) = 109.98ρ-0.268, w(D) = 121.88ρ-0.363, ρBulk(L) = -0.111ln(ρ) + 1.7605, ρBulk(D) = 2.5991ρ-0.037 with determination coefficients, R2 that varied from 0.5643 – 0.8927. This study was successfully demonstrated that the consistency of ERV was greatly influenced by the variation of soil basic physical properties (d, w, ρBulk, LL, PL and PI). Finally, the reliability of the ERV result interpretation can be enhanced due to its ability to produce a meaningful outcome based on supported data from basic geotechnical properties

Integrated Design and Implementation of Embedded Control Systems with Scilab

Embedded systems are playing an increasingly important role in control engineering. Despite their popularity, embedded systems are generally subject to resource constraints and it is therefore difficult to build complex control systems on embedded platforms. Traditionally, the design and implementation of control systems are often separated, which causes the development of embedded control systems to be highly time-consuming and costly. To address these problems, this paper presents a low-cost, reusable, reconfigurable platform that enables integrated design and implementation of embedded control systems. To minimize the cost, free and open source software packages such as Linux and Scilab are used. Scilab is ported to the embedded ARM-Linux system. The drivers for interfacing Scilab with several communication protocols including serial, Ethernet, and Modbus are developed. Experiments are conducted to test the developed embedded platform. The use of Scilab enables implementation of complex control algorithms on embedded platforms. With the developed platform, it is possible to perform all phases of the development cycle of embedded control systems in a unified environment, thus facilitating the reduction of development time and cost.Comment: 15 pages, 14 figures; Open Access at http://www.mdpi.org/sensors/papers/s8095501.pd

Three-phase separator simulator

In this thesis, the goal is to compare the UIT model in the energy lab to the simulation program Aspen HYSYS. The model in the lab has consist of a three-phase separator tank with a weir, and two-component of water and cooking oil, It was made by two students at UIT in 2018. In this simulation, there will be three different variables to determine the accuracy of the simulation. The flow in and out, the purity and the layer height. The HYSYS simulation of a model used in scares, but valuable information can be achieved. There will be theoretical calculations to confirm the simulations. The challenges are the lack of the phase of gas, additional information on oil has been used, and what are the pressure and temperature. In addition, there are made tasks for the student that have challenges and understand the working of the three-phase process and recreate the model that has been made. Keywords: Simulation, three-phase separation tank, ASPEN HYSYS, MATLA

Dynamic Modeling for a Second Order System ofTanks in Series Non-interacting System Using Simulink in Matlab

Series non-interacting tank system is a series of tank that been align together after each other. The level of both tank are the control variables in this system meanwhile the flowrate inlet and outlet of the tanks are the manipulated variables. Since a tank is design at certain limit, thus the level of the tank must always be monitored so that it would not go beyond the design level as this will give bad effect to the tank itself. Indeed it is also a safety precaution; excess level may cause spillage and spoil the tank as well as the product quality. For the purpose of study, this dynamic model has been developed to give a reliable mathematical model so that it will give an ease for future system monitoring. Level of each tank really depending on one another as the tanks is arranged in series; furthermore the level must also be controlled. In this dynamic modeling, two tanks in series have been taken as an example. This is only to grab the concept of series tank. The number oftank can be extended but still the two series tank concept is taken as a baseline. And this is the reason why this experiment is using the second order system. Simulink is the core software for this dynamic modeling. The simulation as well as the PID tuning is done using this software. The values that have been substituted in this simulation are taken from a real figure from PETRONAS in Dexter. However, there are still some assumptions have beenmade such as pumps stroke, valve opening etc. Those figures also been varied in order to study its trending. But when come to the real application, those values canbe substituted back withthe actual value from plant

Prediction of PID control model on PLC

PID (Proportional Integral Derivative) is a control algorithm that mostly used in industry. However, users have never known what the PID model that used inside the PLC. By knowing the PID model that used in PLC, users will have more choice in determining the more appropriate tuning algorithm. Also, users can use MATLAB to perform analysis and can implement it to PLC. Through OPC Server (Object Linking and Embedding for Process Control Server) as a software interface, programs on a windows operating system can communicate with industry devices universally. PID model prediction method is done by comparing the output of the plant controlled by PID model in PLC and PID model in SIMULINK MATLAB using OPC Server intermediaries. Based on comparison result in graph and analysis using integral error method, PLC M221 using Parallel PID model and PLC S7-1200 using Ideal PID model

Simulation and visualization platform integrated under hardware control systems for a reconfigurable process control

Simulation and visualization platform integrated under hardware control systems for a reconfigurable process control

Variable Speed Simulation for Accelerated Industrial Control System Cyber Training

It is important for industrial control system operators to receive quality training to defend against cyber attacks. Hands-on training exercises with real-world control systems allow operators to learn various defensive techniques and see the real-world impact of changes made to a control system. Cyber attacks and operator actions can have unforeseen effects that take a significant amount of time to manifest and potentially cause physical harm to the system, making high-fidelity training exercises time-consuming and costly. This thesis presents a method for accelerating training exercises by simulating and predicting the effects of a cyber event on a partially-simulated control system. A hardware-in-the-loop system comprised of a software-modeled water tank and a commercially-available programmable logic controller is used to demonstrate the feasibility of this method. The results demonstrate the system\u27s speedup capability which allows users to accurately simulate the effects of a cyber event at speeds faster than real-time

The Design of Automated Control System for Wastewater Treatment Plant

Currently, because of the daily human waste to environment, the water, the air breathed by humans are not clean. Even though most developed countries are having improved system of wastewater treatment system, the treatment process is done partly by human and sometimes problem can occur during the process which reduces the quality of the effluent wastewater. In order to overcome human errors, the use of digital computer to control the process of the wastewater treatment processes is needed. This report discusses about the process of wastewater treatment process and how to design the automated control system for the system. Domestic wastewater can be treated in many ways: physical, chemical and biological unit processes. Since the wastewater treatment process is vast and at the same time, the most important process in wastewater treatment, activated sludge process, will be discussed. Automated control system design for the plant is also built using activated sludge wastewater treatment proces