Multivariable pid control tuning based on optimization technique for wastewater treatment plant

Control designs of wastewater treatment plants (WWTP) become significant nowadays. This is due to the changes in parameters and influent characteristics. WWTP involve a multivariable process which is highly complex and tuning of the control is not easy. In this work, proportional-integral-derivatives (PID) controllers is used. Through a proper tuning of PID controller will result in better closed loop performance of the system. The PID tuning parameters used in this work have been obtained by optimization technique. Two types of optimization method used; particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The tuning parameters have been obtained and the multivariable PID control has been applied to WWTP. The simulation results show better improvement in closed loop performance

Battery Parameters Identification Analysis using Periodogram

Batteries are essential components of most electrical devices and one of the most important parameters in batteries is storage capacity. It represents the maximum amount of energy that can be extracted from the battery under certain specified condition. This paper presents the analysis of charging and discharging battery signal using periodogram. The periodogram converts waveform data from the time domain into the frequency domain and represents the distribution of the signal power over frequency. This analysis focuses on four types of batteries which are leadacid (LA), lithium-ion (Li-ion), nickel-cadmium (Ni-Cd) and nickel-metal-hydride (Ni-MH). This paper used battery model from MATLAB/SIMULINK software and the nominal voltage of each battery is 6 and 12V while the capacity is 10 and 20Ah, respectively. The analysis is done and the result shows that varying capacity produce different power at a frequency and voltage at DC component

Feature Selection Analysis of Chewing Activity Based on Contactless Food Intake Detection

This paper presents the feature selection methods for chewing activity detection. Chewing detection typically used for food intake monitoring applications. The work aims to analyze the effect of implementing optimum feature selection that can improve the accuracy of the chewing detection.  The raw chewing data is collected using a proximity sensor. Pre-process procedures are implemented on the data using normalization and bandpass filters. The searching of a suitable combination of bandpass filter parameters such as lower cut-off frequency (Fc1) and steepness targeted for best accuracy was also included. The Fc1 was 0,5Hz, 1.0Hz and 1.2H, while the steepness varied from 0.75 to 0.9 with an interval of 0.5. By using the bandpass filter with the value of [1Hz, 5Hz] with a steepness of 0.8, the system’s accuracy improves by 1.2% compared to the previous work, which uses [0.5Hz, 5Hz] with a steepness of 0.85. The accuracy of using all 40 extracted features is 98.5%. Two feature selection methods based on feature domain and feature ranking are analyzed. The features domain gives an accuracy of 95.8% using 10 features of the time domain, while the combination of time domain and frequency domain gives an accuracy of 98% with 13 features. Three feature ranking methods were used in this paper: minimum redundancy maximum relevance (MRMR), t-Test, and receiver operating characteristic (ROC). The analysis of the feature ranking method has the accuracy of 98.2%, 85.8%, and 98% for MRMR, t-Test, and ROC with 10 features, respectively. While the accuracy of using 20 features is 98.3%, 97.9%, and 98.3% for MRMR, t-Test, and ROC, respectively. It can be concluded that the feature selection method helps to reduce the number of features while giving a good accuracy

Lithium-ion Battery Parameter Analysis Using Spectrogram

Nowadays, energy storage improves the reliability and efficiency of electric utility system. The most common device used for storing electrical energy is battery. Obtaining an accurate data of battery parameter is important because it will be avoid unexpected system interruption and prevent permanent damage to the internal structure of the batteries. The objective of this study is to apply time-frequency distribution (TFD) which is spectrogram technique in analysis of voltage charging and discharging signal for lithium-ion battery. Spectrogram represents the battery signal in time frequency representation (TFR) which is appropriate to analyze the signal before displaying the instantaneous RMS voltage (Vrms), direct current voltage (VDC) and alternating current voltage (VAC) parameter value. This paper focuses on lithium-ion (Li-ion) battery with nominal voltage of 6 and 12V and various storage capacities from 5 to 50Ah. The battery model is implemented in MATLAB/SIMULINK. From the results, the Li-ion battery parameter could be identified using spectrogram

Lead Acid Battery Analysis using S-Transform

This paper proposes a new signal processing technique using time-frequency distribution (TFD), namely S-transform (ST) for battery parameters estimation. Compared to other TFDs such as short time Fourier transform (STFT) and wavelet transform (WT), ST technique offers more promising results in a low frequency application analysis, especially battery. The results of the ST are the parameters of instantaneous means square voltage (VRMS (t)), instantaneous direct current voltage (VDC (t)) and instantaneous alternating current voltage (VAC (t)) extracted from the time-frequency representation (TFR). Simulation through MATLAB has been conducted using equivalent circuit model (ECM), using 12 V lead acid (LA) battery with capacities from 1.0 Ah to 10.0 Ah. For the battery model, charging/discharging signal has been used to estimate the battery parameters from the ST technique to determine battery characteristics. From the signal characteristics of battery capacity versus VAC (t) obtained, new equation is proposed based on the curve fitting tool. The advantage of this technique embraces a better capability in estimating battery parameters at low frequency component, resulting in better frequency and time resolutions compared to previous TFDs

Lead Acid Battery Analysis using Spectrogram

Battery is an alternative option that can be substituted for future energy demand. Numerous type of battery is used in industries to propel portable power and its makes the task of selecting the right battery type is crucial. These papers discuss the implementation of linear timefrequency distribution (TFD) in analysing lead acid battery signals. The time-frequency analysis technique selected is spectrogram. Based on, the time-frequency representations (TFR) obtain, the signal parameter such as instantaneous root mean square (RMS) voltage, direct current voltage (VDC) and alternating current voltage (VAC) are estimated. The parameter is essential in identifying signal characteristics. This analysis is focussing on lead-acid battery with nominal battery voltage of 6 and 12V and storage capacity from 5 until 50Ah, respectively. The results show that spectrogram technique is capable to estimate and identify the signal characteristics of Lead Acid battery

PID Control Tuning via Particle Swarm Optimization for Coupled Tank System

This paper presents the use of meta-heuristic technique to obtain three parameters (KP, KI and KD) of PID controller for Coupled Tank System (CTS). Particle Swarm Optimization (PSO) is chosen and Sum Squared Error is selected as objective function. This PSO is implemented for controlling desired liquid level of CTS. Then, the performances of the system are compared to various conventional techniques which are Trial and Error, Auto-Tuning, Ziegler-Nichols (Z-N) and Cohen-Coon(C-C) method. Simulation is conducted within Matlab environment to verify the transient response specifications in terms of Rise Time (TR), Settling Time (TS), Steady State Error(SSE) and Overshoot (OS). Result obtained shows that performance of CTS can be improved via PSO as PID tuning methods

Analysis of Transient Response for Coupled Tank System via Conventional and Particle Swarm Optimization (PSO) Techniques

This paper investigates the implementation of conventional and Particle Swarm Optimization (PSO) techniques to obtain optimal parameters of controller. In this research, the transient responses of the Coupled Tank System (CTS) are analyzed with the various conventional and metaheuristic techniques which are Trial and Error, Auto-Tuning, Ziegler-Nichols (ZN), Cohen-Coon (CC), standard PSO and Priority-based Fitness PSO (PFPSO) to tune the PID controller parameters. The purpose of this research is to maintain the liquid at the specific or required height in the tank. Simulation is conducted within Matlab environment to verify the performance of the system in terms of Settling Time (Ts), Steady State Error (SSE) and Overshoot (OS). It has been demonstrated that implementation of meta-heuristic techniques are potential approach to control the desired liquid level and improve the system performances

Compilation and Code Optimization for Data Analytics

The trade-offs between the use of modern high-level and low-level programming languages in constructing complex software artifacts are well known. High-level languages allow for greater programmer productivity: abstraction and genericity allow for the same functionality to be implemented with significantly less code compared to low-level languages. Modularity, object-orientation, functional programming, and powerful type systems allow programmers not only to create clean abstractions and protect them from leaking, but also to define code units that are reusable and easily composable, and software architectures that are adaptable and extensible. The abstraction, succinctness, and modularity of high-level code help to avoid software bugs and facilitate debugging and maintenance. The use of high-level languages comes at a performance cost: increased indirection due to abstraction, virtualization, and interpretation, and superfluous work, particularly in the form of tempory memory allocation and deallocation to support objects and encapsulation. As a result of this, the cost of high-level languages for performance-critical systems may seem prohibitive. The vision of abstraction without regret argues that it is possible to use high-level languages for building performance-critical systems that allow for both productivity and high performance, instead of trading off the former for the latter. In this thesis, we realize this vision for building different types of data analytics systems. Our means of achieving this is by employing compilation. The goal is to compile away expensive language features -- to compile high-level code down to efficient low-level code