Neural Network-based Hybrid Estimator for Estimating Concentration in Ethylene Polymerization Process: An Applicable Approach

Estimation of a monomer concentration of an ethylene polymerization process has been a challenging problem due to its highly nonlinear behavior and interaction among state variables.  Applying of an extended Kalman filter (EKF) to provide the estimates of the concentration based on measured bed temperatures has usually been prone to errors. Here, alternatively, neural network-based hybrid estimators have been developed and classified into three structures which integrating of either EKF or Kalman filter (KF) to neural network (NN) to provide the estimates. The NNs are integrated to provide the estimates’ error or concentration’s estimates corresponding to individual structure for reducing the estimation error. Simulation results have shown that the hybrid estimators can provide good estimates under nominal condition and disturbance cases. However, in dealing with noises, the NN-KF hybrid estimator gives superior robustness with smooth and accurate estimated values

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

In a pharmaceutical industry, batch extractive distillation (BED), a combination process between extraction and distillation processes, has been widely implemented to separate waste solvent mixture of acetone-methanol because of minimum-boiling azeotrope properties. Normally, water is used as solvent and semi-continues mode is proposed to improve purity of acetone. The solvent is charged into the BED column until the purity of a desired product is achieved. After the total reflux start-up period is ended, a dynamic optimization strategy is applied to determine an acetone distillate composition profile maximizing the weight of the distillate product (acetone). The acetone distillate composition profile is used as the set point of neural network model-based controllers: the neural network direct inverse model control (NNDIC) and neural network based model predictive control (NNMPC) in order to provide the acetone composition with the purity of 94.0% by mole within 9.5 hours. It has been found that although both NNDIC and proportional integral derivative (PID) control can maintain the distillate purity on its specification for the set point tracking and in presence of plant uncertainties, the NNMPC provides much more satisfactory control performance and gives the smoothest controller action without any fluctuation when compared to the NNDIC and PID

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

A main difficult task in batch crystallization is to control the size distribution of crystal products. Complexity and highly nonlinear dynamic behavior directly affect to model-based control strategies which heavily depend on the rigorous knowledge of crystallization. In this work, neural network-based model predictive control and inverse neural network control strategies are proposed and integrated with an optimization based on neural network-based hybrid model to control temperatures of a purified terephthalic acid batch crystallizer. A neural network-based hybrid model of the batch crystallizer is developed to provide nonlinear dynamic responses used in optimization algorithm for finding an optimal temperature profile related to the quality of a crystal product. Then, the obtained optimal profile is used as set points of the proposed control strategies for improving the crystal product quality. The performances and robustness of the proposed controllers are evaluated in several cases such as for set point tracking and plant/model mismatches. Simulation results show that the neural network-based model predictive control gives the best control performance among the inverse neural network control and a conventional PID controller in all cases.A main difficult task in batch crystallization is to control the size distribution of crystal products. Complexity and highly nonlinear dynamic behavior directly affect to model-based control strategies which heavily depend on the rigorous knowledge of crystallization. In this work, neural network-based model predictive control and inverse neural network control strategies are proposed and integrated with an optimization based on neural network-based hybrid model to control temperatures of a purified terephthalic acid batch crystallizer. A neural network-based hybrid model of the batch crystallizer is developed to provide nonlinear dynamic responses used in optimization algorithm for finding an optimal temperature profile related to the quality of a crystal product. Then, the obtained optimal profile is used as set points of the proposed control strategies for improving the crystal product quality. The performances and robustness of the proposed controllers are evaluated in several cases such as for set point tracking and plant/model mismatches. Simulation results show that the neural network-based model predictive control gives the best control performance among the inverse neural network control and a conventional PID controller in all cases

Modeling of metallocene catalyzed propylene polymerization in fluidized bed reactors

A study was performed to improve the model for metallocene catalyzed polyolefin polymerization in fluidized bed reactor by adapting multi-scale modeling approach. Monomer concentration and reactor temperature was predicted using kinetic model of polypropylene homopolymerization coupled with well mixed reactor models of fluidized bed reactor. Well mixed model typically used for Ziegler-Nata was selected as supported homogeneous metallocene exhibited heterogeneous property similar to ZN catalyst. Result of simulation showed that model was able to predict reaction temperature accurate with around 3% over-prediction of reactor temperature, which is more accurate than previous model. Model predicted decrease in final monomer concentration from 0.9929 mol/s to 0.986 mol/s when initial reactor was raised from 25C to 75C

Effect of an Inquiry-Based Blended Learning Module on Electronics Technology Students' Academic Achievement

Technological advances have led to a change in teaching strategies applied in Technical and Vocational settings. An effective teaching strategy is needed to address issues encountered in the traditional learning process of Electronics Technology Students at Malaysian Vocational College. Blended learning is one of the best teaching strategies for Electronic Technology courses as it is in line with the 21st-century learning, especially in promoting student-centred and life-long learning. This study looks at the impact of an Inquiry-Based Blended Learning (IBBL) module on the students’ achievement in the Industrial Electronics Equipment Problem Solving (IEEPS) Course, in an Electronic Technology Program. This study uses an experimental study design through the quasi-experimental method to evaluate the effectiveness of the module. The comparison of achievement between an experimental group and a control group was conducted based on the pre-test and post-test protocol. The findings of the evaluation phase through the t-test showed that there was a significant difference (p<.05) between the experimental group and the control group. This indicated that using an Inquiry-Based Blended Learning Module was effective to help the student to improve their achievement in Industrial Electronics Equipment Problem Solving Course. Therefore, the inquiry-based blended learning module has the potential to be applied by instructors and students in the Vocational College setting

The relationship between performance and graphic presentation in unit trusts' annual reports: Malaysian evidence

This study investigates the use and abuse of graphs in the annual reports of unit trust companies.It is found that 78% of companies use graphs and that 2.1 is the mean number of graphs per graph-using companies.The most commonly graphed financial variables are asset allocation, performance, investment and fund size.Line and pie graphs are more popular than bar and column.Thus, in contrast to previous studies of graphs in annual reports, no relationship is found between performance and graphic presentation in unit trusts’ annual reports.The result may suggest that graphic presentation in unit trust’s annual report is normally dependent on the discretion of company’s management

Industry-specific knowledge that vocational teachers should know and be able to do to prepare a job-ready workforce

Industries’ activity and skill demands are changing so rapidly that it is an enormous task for teachers to simply keep on top of these developments. This paper studies the industry-specific knowledge that vocational teachers should know and be able to do to be competent teachers. This industry-specific knowledge will help vocational teachers produce a skilled workforce that meets the demands of the Malaysian labor market. To identify the necessary industry-specific knowledge for Malaysian vocational teachers, a modified Delphi study has been conducted on 25 expert panelists from Malaysian vocational colleges and local universities. The experts were asked to rate the eleven-predetermined industry-specific knowledge. In addition, the panelists were asked to suggest any industry-specific knowledge that they believed Malaysian vocational teachers needed to know. Through two rounds of Delphi surveys, 13 areas of industry-specific knowledge have been identified necessary for Malaysian vocational teachers. That industry-specific knowledge will enrich vocational college instructors’ teaching competencies, especially regarding planning and preparing their teaching activities so vocational students will have the up-to-date knowledge and skills required by potential employers

Development of controllers for a nonlinear quarter car active suspension system

In this paper, the development of automatic controllers for an active suspension system using a quarter car model is described. The main aim of the controller is to minimise the body displacement, velocity and acceleration while keeping the rattle space and other movement and forces related to the suspension system in their limited ranges. The suspension system is difficult to control since the characteristics of spring, damping force, 'tyre lift' and the road model are mostly nonlinear. A passive suspension system responds only to the deflection of the strut, while the active system setup can put energy into the system at an appropriate time, in a way or amount that is right for all the variables in the system

Acute effect of copper on Puntius javanicus survival and a current opinion for future biomarker development

Puntius javanicus experimental groups were exposed with to different concentration of copper (II) sulfate for 96 hours. Their mortality was recorded to determine LC50 value of copper concentration based on arithmetic, logarithmic and probit graphic analyses. The results obtained from these three mathematical analyses were 11.37±0.58, 11.01±0.73 and 10.68 mg/L, respectively. From the present study, we suggested that in the future, the range of 0 to 5.0 mg/L can be used to study the effect of copper concentration on fish activity at biochemical and physiological levels. Based on probit analysis, this maximum range is lower than LC10 value i.e. 6.11 mg/L. Therefore, it can be positively hypothesised that there would be no mortality occur except for several symptoms of adverse effects beyond of 5.0 mg/L treatment

A correlation between performance and graphic presentation in unit trust's annual report

This paper investigates the extent use of graphs, the types of graphs and the types of information being presented graphically in unit trust’s annual reports. The paper formulates and test hypothesis concerning selectivity in the use of graphs. Results show that 78 per cent of unit trust’s annual reports use graphs and that 2.1 is the mean number of graphs per graph-using companies. The most commonly graphed financial variables are asset allocation, performance, investment and fund size. Line and pie graphs are more popular than bar and column. Thus, contrast to studies of graphs in annual report, no correlation is found between performance and graphic presentation in unit trust’s annual report. This is because the graphic presentation in unit trust’s annual report is normally dependent on discretion of company’s management