Measurement and analysis of water/oil multiphase flow using electrical capacitance tomography sensor

The paper investigates the capability of using a portable 16-segmented Electrical Capacitance Tomo-graphy (ECT) sensor and a new excitation technique to measure the concentration profile of water/oil multiphase flow. The concentration profile obtained from the capacitance measurements is capable of providing images of the water and oil flow in the pipeline. The visualization results deliver information regarding the flow regime and concentration distribution of the multiphase flow. The information is able to help in designing process equipment and verifying the existing computational modeling and simu-lation techniques

An investigation on chemical bubble column using ultrasonic tomography for imaging of gas profiles

In this paper, we carried out a tomographic investigation of a chemical bubble column using ultrasonic sensor. The ultrasonic tomography sensing array was constructed to operate in transceiver-mode and was clamped on the exterior circumference of the column. The time-of-flight and arrival-time analysis was studied to obtain the signal information. Some experiments were carried out using known static profiles and were compared with the actual profiles. The findings showed promising results where the sensing array could detect gas bubble profiles down to 3 mm in diameter, and the conclusions were made at the end of the paper

Agarwood oil quality classifier using machine learning

Agarwood oil is known as one of the most expensive and precious oils being traded. It is widely used in traditional ceremonies and religious prayers. Its quality plays an important role on the market price that it can be traded. This paper proposes on a proper classification method of the agarwood oil quality using machine learning model k-nearest neighbour (k-NN). The chemical compounds of the agarwood oil from high and low quality are used to train and build the k-NN classifier model. Correlation reduce the dimension of the data before it is being fed into the model. The results show a very high accuracy (100%) model trained and can be used to classify the agarwood oil quality accurately. Keywords: agarwood oil; k-nearest neighbours; quality; machine learning

Analysis of crude palm oil composition in a chemical process conveyor using Electrical Capacitance Tomography

In this work, we aim to analyse the capability of using a 16-segmented Electrical Capacitance Tomo-graphy (ECT) sensor system to monitor the internal composition of a chemical process conveyor that carries crude palm oil (CPO) multiphase flow. The source used to excite the electrodes is a differential potential, instead of the conventional single potential source, in order to obtain an improved sensitivity of the sensor, especially in the central area of the pipe. This system aims to recognise the phase con-centration of the flow. The attained concentration profile that is received from the capacitance mea-surements is capable of providing an image of the liquid and liquid mixture in the pipeline, making the separation process (between oil and liquid waste) much easier and the CPO's quality can be dependably monitored. Experimental results and analysis are presented, and the new excitation technique is shown to provide better sensor sensitivity in the central pipe area. The visualisation results deliver information regarding the flow regime and concentration distribution in a two-phase flow-rate measurement system incorporating a liquid flow-measuring device. The information obtained will assist the design of process equipment, and the verification of existing computational modelling and simulation technique

Steam temperature control of essential oil extraction system using Fuzzy-Fopi controller

This research proposed a closed-loop temperature control using a self-tuning fuzzy fractional-order PI (FOPI) controller to overcome the problem. The controller will regulate the steam temperature at a desired level to protect the oil from excessive heat. Self capability of fuzzy rules was found to facilitate the tuning using only information about output error and rate of output error. The proposed controller was found to be more in terms of energy consumption and produce better response compared to self-tuning fuzzy PI. Essential oil quality assessment was also performed for citronella oil samples that were extracted at 85ºC and 100ºC (uncontrolled). Significant improvement had been observed in the sample extracted at 85ºC based on physical and chemical evaluations using refractive index measurement and GC-MS analysis.Keywords: fuzzy control; fractional-order PID; essential oil extraction; hydro-steam distillation

The pre-processing technique and parameter adjustment influences on NARX-based BPSO structure for time-varying water temperature modelling

No AbstractKeywords: identification; NARX; particle swarm optimization; distillation colum;  temperatur

NARX-based BPSO modelling for time-varying steam temperature of steam distillation pilot plant

This paper focuses on a nonlinear modelling for a time-varying process of steam temperature by employing a polynomial Nonlinear Auto-Regressive with Exogenous Input (NARX) structure based on Binary Particle Swarm Optimization (BPSO) algorithm. The system identification time-varying steam temperature data was collected from Steam Distillation Pilot Plant. Three models’ criterion were implemented: Akaike Information Criterion, Model Descriptor Length (MDL) and Final Prediction Error (FPE) for optimization process of NARX-based BPSO modelling. The results demonstrated that the FPE criterion model was presented a slightly better model with lowest CRV from the testing set, small fitness value and a  minimum number of parameter in the output model. The accuracy was evaluated by the high R-squared, small MSE value and passed all the correlation and histogram tests.Keywords: identification; distillation column; temperature; NARX; particle swarm optimization.

Comparison between MPC and PID control for compact hydro distillation process

This paper presents water temperature of a hydro distillation that have been modelled by using linear ARX Modal. Based on the modal obtained, a model predictive controller and PID controller have been developed. Both controller undergone the performance of controller tests that includes set point, set point change and load disturbance. The aim of those three performance tests are to test the robustness among those controllers. In this study, the analyzed via simulation. The criteria of transient responses which are rise time, settling time and percentage of overshoot is robustness among those controllers. In this study, the comparative of the controller performances were evaluated and analyzed via simulation. The criteria of transient responses which are rise time, settling time and percentage of overshoot is chosen to evaluate the robustness of the controller performance. The simulation result shows that the better performance of overall robustness test have been conquered by MPC with compared to PIDCC and PIDZN.  Keywords: ARX model; system identification; model predictive controller; PID controller