Improved results on frequency-weighted balanced truncation and error bounds

In this paper, we present some new results on frequency-weighted balanced truncation which is a significant improvement on Lin and Chiu's frequency-weighted balanced truncation technique. The reduced-order models, which are guaranteed to be stable in the case of double-sided weighting, are obtained by direct truncation. Two sets of simple, elegant and easily calculatable a priori error bounds are also derived. Numerical examples and comparison with other well-known techniques show the effectiveness of the proposed technique

Variable Speed Drives: Energy Saving and SCADA System

In this paper, implementation of variable speed drives (VSD) in a water treatment plant (WTP) and its capability in energy consumption reduction is presented. Conducted in Titi WTP, Negeri Sembilan, Malaysia, the project was run for four years, i.e. from early 2011 until the end of 2014. In the duration of four years, four VSDs are installed in the raw water pump house (RWPH) and treated water pump house (TWPH) of the plant with a power rating of 130kW and 260kW respectively. The electrical energy consumption of the plant before VSD installation was recorded as reference and a comprehensive supervisory control and data acquisition (SCADA) system was installed to remotely and automatically control the pump operation of the WTP. From The results obtained, it can be observed that, at the end of the four years, a saving of 26.64% and 24.58% for the energy consumption (kWh) and the overall cost (RM) respectively is achieved. Meanwhile, the payback period or the return on investment (ROI) period is calculated to be 3.4 years

Preliminary study of an electro-mechanical artificial insemination device for small ruminants

Artificial Insemination (AI) technique is used worldwide in goats breeding, especially in intensive systems reproduction control. AI is a low cost technique and simple to perform, however the labor cost is steep since the need experts are necessity. The immediate need to produce good profits in goats production, has initiated a development and initialization of an electro-mechanical artificial insemination device prototype for goats is initially developed. This device is able to increase the accuracy of semen deposited to the cervix by visualizing the os. This is realized by a built-in circuit utilizing a PIC as its brain and functions well as pre-programmed in the PIC. Furthermore, a continuous use of this device could significantly reduce the labor cost. However, since the operation time is relatively slow, further improvements are required

Proactive and Predictive Maintenance Strategies and Application for Instrumentation & Control in Oil & Gas Industry

Instrumentation & Control Systems have gone through various revolutions in the oil and gas industry. The start of the industry was based on pneumatic controllers, operating with instrument air or instrument gas at 0.2 to 1.0 barg and the Instrument Protective System (IPS) was relay based. This system provided almost zero information or data that can be used to predict failure and had a higher unrevealed failure. The next phase of instrumentation was migration into the electronics era. This allowed for the migration from a pneumatic system to the application of electronics field device operation on 4-20mA connected to Distributed Control System (DCS) and IPS. Further development of 4-20mA communication protocol allowed for the development of digital superimpose communication called Highway Addressable Remote Transducer (HART). The data that is transmitted in HART protocol provides sufficient data and information to predict the health and functionality of the instrumentation. Changes in the maintenance philosophy from reactive maintenances to proactive or predictive maintenances, resulted in reduced downtime by scheduling maintenance to optimize the working window. This work process allows for greater productivity of assets, both human and capital. Dashboards are developed and utilised to alert fault detection and loss of redundancy, whereby data is relayed from DCS and IPS to a web-based system. Available tools being developed in the industry and application of IoT within the industry allows for real-time field device health monitoring and an engine to predict deviations. The strength and weakness of the various proactive or predictive maintenance strategies are compared and summarized, including scope for future research and application in the industry

Modeling of activated sludge process using various nonlinear techniques: a comparison study

This paper presents a comparison study between radial basis function neural network (RBFNN), feed forward multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy (ANFIS) technique to model the activated sludge process (ASP). All of these techniques are based on the nonlinear autoregressive with eXogenous input (NARX) structure. The ASP inputs and outputs data are generated from activated sludge model 1 (ASM1). This work will cover the dissolved oxygen (DO), substrate and biomass modeling. The performances of the model are evaluated based on R2, mean square error (MSE) and root mean square error RMSE. The simulation result shows that ANFIS with NARX structure given a better performance compared with the other modeling techniques

Neural Network Model Development with Soft Computing Techniques for Membrane Filtration Process

Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IW-PSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO

Modeling of waste water treatment plant via system ID & model reduction technique

This paper investigates the application of Model Order Reduction (MOR) technique to Waste Water Treatment Plant (WWTP) system. The mathematical model of WWTP is obtained by using System Identification. In this paper, Prediction Error Estimate of Linear or Nonlinear Model (PEM) is proposed as the System Identification method which is used to find the parameter of linear or nonlinear system in state-space model from an experimental input­ output data WWTP. The result shows that the estimated model of WWTP is a high order system with good best fit with 91.56% and80.19% compared to the original experimental model. To simplify the obtained model,the MOR technique is proposed to reduce the high order system to lower order system while still retaining the characteristics of the original system. In this paper, the balanced truncation and Frequency Weighted Model Reduction (FWMR) are proposed to obtain a lower order WWTP model. The result shows that by MOR techniques, the higher WWTP system can be simplified to lower order system with a low error of the reduced system. The result of reduced model will be represented in sigma graph and numerical value

Dynamic model development for submerged membrane filtration process using recurrent artificial neural network with control application

Modeling of membrane filtration process is challenging task because it is involves many interactions from biological and physical operation behavior. Membrane fouling in filtration process is too complex to understand and to derive a robust model is not possible. The aim of this paper is to study the potential of neural network based dynamic model for submerged membrane filtration process. The purpose of the model is to represent the dynamic behavior of the filtration process therefore suitable control strategy and tuning can be developed to control the filtration process more effectively. In this work, a recurrent neural network (RNN) structure was employed to perform the dynamic model of the filtration process. The random step was applied to the suction pump to obtained the permeate flux and Transmembrane Pressure (TMP) dynamic. The model was evaluated in term of %R2, root mean square error (RMSE,) and mean absolute deviation (MAD). Proportional integral derivative (PID) controller was implemented to the model for different control strategies and several tuning gains were tested for the effective filtration control. The result of proposed modeling technique showed that the RNN structure is able to model the dynamic behavior of the filtration process below critical flux condition. The developed model also can be a reliable assistance for the control strategy development in the filtration process

Face recognition on bag locking mechanism

With the emergent of biometric technology, people are no longer afraid to keep their important things in the safe box or room or even facility. This is because; human beings have unique features that distinguish them with other people. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called ‘Eigenfaces’, which are actually the principal components of the initial training set of face images. In this report, thorough explanation on design process of face recognition on bags locking mechanism will be elucidated. The results and analysis of the proposed design prototype also presented and explained. The platform for executing the algorithm is on the Raspberry Pi. There are two artificial intelligent techniques applied to manipulate and processing data which is fuzzy logic and neural networks. Both systems are interdependent with each other, so that it can calculate and analyse data precisely. The receive image from the camera is analysed through the Eigenfaces algorithm. The algorithm is using Principal Component Analysis (PCA) method which comprise of artificial neural network paradigm and also statistical paradigm

Neural network model development with soft computing techniques for membrane filtration process

Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IW-PSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO