Adaptive neuro-fuzzy control of wet scrubbing process

Thenon-linear characteristics of wet scrubbing process have led to the application of intelligent control technique to adequately deal with these complexities by manipulating the liquid droplet size for the effective control of particulate matter (PM) contaminants. This includes the use of adaptive neuro-fuzzy inference system (ANFIS) to design an intelligent controller based on direct inverse model control strategy using default input and output membership functions (gaussmf and linear) and different number of input membership functions. This is followed by training of the fuzzy inference system to obtain inverse model which was tested as the intelligent controller. The controller developed using two-input membership functions have successfully achieved the main target of setting the PM concentration (process output) below the set point which is the allowable World health organization (WHO) emission level for 20g/μm within a short settling time of 2s. © 2015 IEEE

Adaptive neuro-fuzzy control of wet scrubbing process

The nonlinear characteristics of wet scrubbing process have led to the application of intelligent control technique to adequately deal with these complexities by manipulating the liquid droplet size for the effective control of particulate matter (PM) contaminants. This includes the use of adaptive neuro-fuzzy inference system (ANFIS) to design an intelligent controller based on direct inverse model control strategy using default input and output membership functions (gaussmf and linear) and different number of input membership functions. This is followed by training of the fuzzy inference system to obtain inverse model which was tested as the intelligent controller. The controller developed using two-input membership functions have successfully achieved the main target of setting the PM concentration (process output) below the set point which is the allowable World health organization (WHO) emission level for 20g/μm3 within a short settling time of 2s

Ensemble Methods in Environmental Data Mining

Environmental data mining is the nontrivial process of identifying valid, novel, and potentially useful patterns in data from environmental sciences. This chapter proposes ensemble methods in environmental data mining that combines the outputs from multiple classification models to obtain better results than the outputs that could be obtained by an individual model. The study presented in this chapter focuses on several ensemble strategies in addition to the standard single classifiers such as decision tree, naive Bayes, support vector machine, and k-nearest neighbor (KNN), popularly used in literature. This is the first study that compares four ensemble strategies for environmental data mining: (i) bagging, (ii) bagging combined with random feature subset selection (the random forest algorithm), (iii) boosting (the AdaBoost algorithm), and (iv) voting of different algorithms. In the experimental studies, ensemble methods are tested on different real-world environmental datasets in various subjects such as air, ecology, rainfall, and soil

Hardware implementation of ANFIS controller for gas-particle separations in wet scrubber system

Wet scrubber system has been used for the control of gas and particulate matter (PM) emissions from production industries. Due to non-linear characteristics, wet scrubbers are limited to the control of PM that is less than 5μm. In this study, an intelligent control technique based on Adaptive Neuro-Fuzzy Inference System (ANFIS) has been designed using MATLAB software. The ANFIS Controller has the advantage of solving non-linearities in the proposed wet scrubber system by manipulating the scrubbing liquid droplet size for the effective control of particulate matter that is less than 5μm. From the simulation results, the controller was able to set PM concentration below the set-point and provides smooth control response within short settling time. Hardware implementation of the ANFIS controller was performed using prototype wet scrubber system by considering Arduino Duemilanove microcontroller and MATLAB interface. The results show that the intelligent controller has achieved the desired objectives of controlling the PM concentration effectively by setting the value below the set point (20μg/m3) which is the allowable PM concentration standard recommended by World Health Organization

AN ANFIS – BASED AIR QUALITY MODEL FOR PREDICTION OF SO2 CONCENTRATION IN URBAN AREA

This paper presents the results of attempt to perform modeling of SO2concentration in urban area in vicinity of copper smelter in Bor (Serbia), using ANFIS methodological approach. The aim of obtained model was to develop a prediction tool that will be used to calculate potential SO2 concentration, above prescribed limitation, based on input parameters. As predictors, both technogenic and meteorological input parameters were considered. Accordingly, the dependence of SO2concentration was modeled as the function of wind speed, wind direction, air temperature, humidity and amount sulfur emitted from the pyrometallurgical process of sulfidic copper concentration treatment

Adaptive Cooperative Learning Methodology for Oil Spillage Pattern Clustering and Prediction

The serious environmental, economic and social consequences of oil spillages could devastate any nation of the world. Notable aftermath of this effect include loss of (or serious threat to) lives, huge financial losses, and colossal damage to the ecosystem. Hence, understanding the pattern and  making precise predictions in real time is required (as opposed to existing rough and discrete prediction) to give decision makers a more realistic picture of environment. This paper seeks to address this problem by exploiting oil spillage features with sets of collected data of oil spillage scenarios. The proposed system integrates three state-of-the-art tools: self organizing maps, (SOM), ensembles of deep neural network (k-DNN) and adaptive neuro-fuzzy inference system (ANFIS). It begins with unsupervised learning using SOM, where four natural clusters were discovered and used in making the data suitable for classification and prediction (supervised learning) by ensembles of k-DNN and ANFIS. Results obtained showed the significant classification and prediction improvements, which is largely attributed to the hybrid learning approach, ensemble learning and cognitive reasoning capabilities. However, optimization of k-DNN structure and weights would be needed for speed enhancement. The system would provide a means of understanding the nature, type and severity of oil spillages thereby facilitating a rapid response to impending oils spillages. Keywords: SOM, ANFIS, Fuzzy Logic, Neural Network, Oil Spillage, Ensemble Learnin

Air pollution forecasts: An overview

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies

Non-stationary exchange rate prediction using soft computing techniques

Soft computing is widely used as it enables forecasting with fast learning capacity and adaptability, and can process data despite uncertainties and complex nonlinear relationships. Soft computing can model nonlinear relationships with better accuracy than traditional statistical and econometric models, and does not make much assumptions regarding the data set. In addition, soft computing can be used on nonlinear and nonstationary time series data when the use of conventional methods is not possible. In this paper, we compare estimates of the nonstationary USD/IDR exchange rates obtained by three soft computing methods: fuzzy time series (FTS), the artificial neural network (ANN), and the adaptive-network-based fuzzy inference system (ANFIS). The performances of these methods are compared by examining the forecast errors of the estimates against the real values. Compared to ANN and FTS, ANFIS produced better results by making predictions with the smallest root mean square error

Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification

Malaysia is a developing country which relies on the monetary approach to measure poverty. The approach is simple to measure but it is insensitive towards changes of the poor in multiple dimensions such as education, health and living standards especially in urban areas. Several current issues in classifying the urban poor include rigid dichotomy of the poor and non-poor, unable to capture changes that happens in various sub-groups of urban poor population and misclassified poverty indicators. This study developed a multidimensional poverty measurement framework which integrated i) Alkire-Foster approaches in quantification of multidimensional urban poor, ii) Adaptive Neural Fuzzy Inference Systems (ANFIS) to predict classification of urban poor and resolve the misclassification of urban poor and iii) ensemble ANFIS. 300 questionnaires were distributed to targeted households in Bandar Tasik Selatan, Kuala Lumpur. This study started with a comparison of datadriven Fuzzy Rule-Based System (FRBS) with the domain expert comprising FRBS classification. Next, the Alkire-Foster method was introduced which included parameter selection, dual cut off identification and aggregation of the poor. Then, the ANFIS prediction was carried out using various ANFIS combination models such as Genfis 1, Genfis 2 and Genfis 3 to predict the classification of urban poor. This study proceeded to improve the classification by proposing the ensemble ANFIS that included ensemble weighting and ensemble integration method. The performance of this proposed framework was evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), and R-Squared. For validation purposes, this study was reviewed by officers at the Zakat Collection Centre, Kuala Lumpur as the domain experts. The findings showed that the Genfis 3 using Fuzzy C-Means clustering algorithm in ANFIS outperformed all the ANFIS models, by obtaining the least MSE and RMSE values and highest R-Squared. These results included the Health dimension which was excluded in the current poverty measurement. Overall, this study has managed to address the urban poor classification by providing multiple dimensions of the poor and produce robust prediction results

Optimal adaptive neuro-fuzzy inference system architecture for time series forecasting with calendar effect

This paper discusses a procedure for model selection in ANFIS for time series forecasting with a calendar effect. Calendar effect is different from the usual trend and seasonal effects. Therefore, when it occurs, it will affect economic activity during that period and create new patterns that will result in inaccurate forecasts for decision making if not considered. The focus is on the model selection strategy to find the appropriate input variable and the number of membership functions (MFs) based on the Lagrange Multiplier (LM) test. The ARIMAX stochastic model is used at the preprocessing stage to capture calendar variations in the data. The calendar effect observed is the Eid al-Fitr holiday in Indonesia, a country with the largest Muslim population in the world. The data of Tanjung Priok port passengers used as a case study. The result shows that hybrid ARIMAX-ANFIS based on the LM test can be an effective procedure for model selection in ANFIS for time series with calendar effect forecasting. Empirical results show that the use of the calendar effect variable provides more accurate predictions as indicated by smaller RMSE and MAPE values than without the calendar effect variable