Nurhani Kasuan

Essential oil composition of Kaffir lime: Comparative analysis between controlled steam distillation and hydrodistillation extraction process

A controlled steam distillation process was performed to extract essential oil of Kaffir lime. Water temperature in the process was controlled by ON/OFF controller. System performance was quantified based on oil production rate and assessment on the compositions in Kaffir lime leaves and peels respectively. The Kaffir lime essential oil compounds were identified by retention time and percentage area by Gas Chromatography — Mass Spectrophotometry (GC-MS). Results showed the essential oil percentage yield given by modified steam distillation was 1.34% (peels) and 0.43% (leaves) whereas for hydrodistillation yielded 0.16% (peels) and 0.18% (leaves). Based on compositions, oil extracted by controlled steam distillation gain higher percentage of limonene (27.97%) and α/β-pinene (9.82%) compared to hydrodistillation.

Steam temperature control using fuzzy logic for steam distillation essential oil extraction process

This paper presents a fuzzy logic controller (FLC) that has been implemented to control the steam temperature in steam distillation for the extraction oil. The objective of this research is to implement the ARMAX model structure and to design the typical controller using FLC to control the steam temperature as a controlled parameter. Temperature regulation is an importance factor to avoid the degradation of the quality oil produced. PRBS input is triggered to the system and output of steam temperature is modeled using ARMAX model structure. FLC is designed with two inputs and one output which are error, difference error and voltage respectively. There are 49 fuzzy rules that involved in performing the FLC. The experimental result demonstrates that the proposed of FLC achieves the better result in controlling the temperature as compared to the PID controller considering the test on the set point tracking.

Model Reference Adaptive Controller to regulate steam temperature in distillation process for essential oil extraction

This paper presents a simulated Model Reference Adaptive Controller (MRAC) and its application to regulate steam temperature in distillation process for essential oil extraction system. Steam temperature is one of the most significant parameters that can influence the composition of essential oil yield in terms of quality and quantity. Due to parameter variations and changes in operation conditions during distillation, a robust steam temperature controller becomes nontrivial to avoid the degradation of essential oil quality. Initially, the PRBS input is triggered to the system and output of steam temperature is modeled by auto regressive exogenous (ARX) model. The online parameter estimation is implemented using recursive least squares and integrated in MRAC scheme. The performance of MRAC is evaluated. The experimental result demonstrates an efficacy of MRAC applied in distillation process and also conform the stability to the system.

A simulation study of Model Reference Adaptive Control on temperature control of glycerin bleaching process

Temperature plays a major role in determining the effectiveness of bleaching process. Poor temperature control will degrade the end product quality and this problem can be overcome by engaging a robust temperature control. However, since the nature of bleaching process possess large time delay and unknown dynamic variations or uncertainties, it is inherently difficult to design a good temperature controller and simultaneously maintain the desired transient performance aspect. This paper presents the preliminary study of Model Reference Adaptive Control (MRAC) implementation in temperature control of glycerin bleaching process. Open loop step test data is used to construct the Auto-Regressive with Exogenous Input (ARX) corresponding model. The MRAC approach based on Lyapunov rule was tested and analyzed. Simulation results show that, through proper selection of adaptation gain, MRAC is capable to provide excellent temperature regulating performance and also proficient in sustaining the excellent performance under set point change.

Essential oil extraction with automated steam distillation: FMRLC for steam temperature regulation

Fuzzy model reference learning control (FMRLC), a model based controller, was implemented in steam distillation extraction oil process. The FMRLC was designed to regulate steam temperature inside the distillation column during extraction process. Maintaining the temperature to a certain degree for the period of extraction process is very important because the temperature will influence the output quality and quantity of essential oil. In this study, the FMRLC was simulated based on ARX modeled plant to regulate process steam temperature up to 85°C. The simulation was based on tuning method to obtained optimized FMRLC parameters. Then, the simulated result was validated by real process implementation.

Recurrent adaptive neuro-fuzzy inference system for steam temperature estimation in distillation of essential oil extraction process

In this paper, recurrent adaptive neuro-fuzzy inference system (RANFIS) structure has been proposed to solve approximation problem in identifying a global model of steam temperature of packed distillation column in steam distillation essential oil extraction process. The input-output data is acquired from field experimentation via MATLAB Real-time Workshop (RTW) integrated to the plant. The derived RANFIS model is optimized in order to get the optimum ANFIS structure that includes the optimal number of membership function, fuzzy rules, data selection, epoch which gives low computation time and root means squared error (RMSE). Several experiments were carried out using both pseudo random binary sequence (PRBS) and noise as perturbation signals. Performance comparison of RANFIS with ARX model shows that RANFIS identification gives an excellent global modeling method with RMSE of 0.1778 and consumed less computation or training time.

A discrete model reference adaptive control for temperature tracking in steam distillation process

This paper demonstrates the performance of Model Reference Adaptive Controller (MRAC) using Lyapunov and MIT schemes applied in steam distillation process in real-time and simulated conditions. The integral limits in MRAC were varied during the experiment. Simulated results shows that low SSE and RMSE were obtained when adaptation gain and integral limit were set to 0.003 and +0.01 respectively. However, in real time control, low SSE and RMSE were obtained when the integral limit was eliminated in MRAC Lyapunov and MRAC MIT control structure.

Model reference adaptive control (MRAC) without integral for glycerin bleaching process

This paper presents the design of a model reference adaptive control (MRAC) for regulating temperature for glycerin bleaching process. Two well known MRAC designs which are MIT and Lyapunov approach are considered. The model of the system is developed based on Auto-Regressive with Exogenous Input (ARX) model. To prevent MRAC from windup problem and to provide robust performance simple modification has been proposed which by removing the pure integral from the original adaptation laws of MRAC. The simulation results shown that the proposed approach was significantly improved the performance MRAC and provide robust performance in regulating temperature for glycerin bleaching process. The result also shows that there is no significant difference performance between modified MIT MRAC and modified Lyapunov MRAC.

Neural network based model reference adaptive control (NMRAC) for steam temperature regulation in steam distillation process

This paper presents a neural network controller in model reference adaptive controller (MRAC) for steam temperature regulation in steam distillation process. Steam temperature is one of the most significant parameters that can influence the composition of essential oil yield. Due to parameter variations and changes in operational conditions during distillation, a robust steam temperature controller becomes nontrivial to avoid the degradation of essential oil quality. Initially, the PRBS input is fed to the system and output of steam temperature is collected and modeled using ARX model. Model reference for adaptive control is designed using first order plus dead time (FOPDT) transfer function. The weights of neural network are updated using Recursive Prediction Error Method (RPEM). The performance of neural network is compared using different number of hidden layer, momentum rate and learning rate. The experimental result demonstrates the proposed neural network using 9 hidden nodes achieves a better control to the system.

Modelling of various meteorological effects on leakage current level for suspension type of high voltage insulators using HMLP neural network

Electrical power system lines sometimes pass along the coastal regions and transverse through the industrial areas of the Peninsular Malaysia. The phenomenon of salt blown from the sea to the land at the coastal area was causing salt deposition to the transformer bushing which contaminating the bushing surfaces and produced leakage current. Hence, it triggering to insulator flashover and finally the hot power arc will damage the bushing. This paper estimates leakage current level by modeling it as a function of various meteorological parameters using Hybrid Multilayered Perceptron Networks (HMLP) with Modified Recursive Prediction Error (MRPE) learning algorithms. The results are also compared with the regression analysis done previously. Meteorological parameters and leakage current data are based on the real measured data collected at YTL Paka Power Station in Terengganu.