Search results for: mining wastewater

Authors: C. Chompuchan, T. Satapanajaru, P. Suntornchot, P. Pengthamkeerati

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Residual dye contents in textile dyeing wastewater have complex aromatic structures that are resistant to degrade in biological wastewater treatment. The objectives of this study were to determine the effectiveness of nanoscale zerovalent iron (NZVI) to decolorize Reactive Black 5 (RB5) and Reactive Red 198 (RR198) in synthesized wastewater and to investigate the effects of the iron particle size, iron dosage and solution pHs on the destruction of RB5 and RR198. Synthesized NZVI was confirmed by transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The removal kinetic rates (kobs) of RB5 (0.0109 min-1) and RR198 (0.0111 min-1) by 0.5% NZVI were many times higher than those of microscale zerovalent iron (ZVI) (0.0007 min-1 and 0.0008 min-1, respectively). The iron dosage increment exponentially increased the removal efficiencies of both RB5 and RR198. Additionally, lowering pH from 9 to 5 increased the decolorization kinetic rates of both RB5 and RR198 by NZVI. The destruction of azo bond (N=N) in the chromophore of both reactive dyes led to decolorization of dye solutions.

Keywords: decolorization, nanoscale zerovalent iron, Reactive Black 5, Reactive Red 198.

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Authors: Laleh Yerushalmi, Fariborz Haghighat, Maziar Bani Shahabadi

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The estimation of overall on-site and off-site greenhouse gas (GHG) emissions by wastewater treatment plants revealed that in anaerobic and hybrid treatment systems greater emissions result from off-site processes compared to on-site processes. However, in aerobic treatment systems, onsite processes make a higher contribution to the overall GHG emissions. The total GHG emissions were estimated to be 1.6, 3.3 and 3.8 kg CO2-e/kg BOD in the aerobic, anaerobic and hybrid treatment systems, respectively. In the aerobic treatment system without the recovery and use of the generated biogas, the off-site GHG emissions were 0.65 kg CO2-e/kg BOD, accounting for 40.2% of the overall GHG emissions. This value changed to 2.3 and 2.6 kg CO2-e/kg BOD, and accounted for 69.9% and 68.1% of the overall GHG emissions in the anaerobic and hybrid treatment systems, respectively. The increased off-site GHG emissions in the anaerobic and hybrid treatment systems are mainly due to material usage and energy demand in these systems. The anaerobic digester can contribute up to 100%, 55% and 60% of the overall energy needs of plants in the aerobic, anaerobic and hybrid treatment systems, respectively.

Keywords: On-site and off-site greenhouse gas (GHG)emissions, wastewater treatment plants, biogas recovery

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Authors: R. A. Salam, M.A. Rodrigues

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The number of features required to represent an image can be very huge. Using all available features to recognize objects can suffer from curse dimensionality. Feature selection and extraction is the pre-processing step of image mining. Main issues in analyzing images is the effective identification of features and another one is extracting them. The mining problem that has been focused is the grouping of features for different shapes. Experiments have been conducted by using shape outline as the features. Shape outline readings are put through normalization and dimensionality reduction process using an eigenvector based method to produce a new set of readings. After this pre-processing step data will be grouped through their shapes. Through statistical analysis, these readings together with peak measures a robust classification and recognition process is achieved. Tests showed that the suggested methods are able to automatically recognize objects through their shapes. Finally, experiments also demonstrate the system invariance to rotation, translation, scale, reflection and to a small degree of distortion.

Keywords: Image mining, feature selection, shape recognition, peak measures.

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Authors: M. A. Sanda, B. Johansson, J. Johansson

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The purpose of this paper is to conceptualize a futureoriented human work environment and organizational activity in deep mines that entails a vision of good and safe workplace. Futureoriented technological challenges and mental images required for modern work organization design were appraised. It is argued that an intelligent-deep-mine covering the entire value chain, including environmental issues and with work organization that supports good working and social conditions towards increased human productivity could be designed. With such intelligent system and work organization in place, the mining industry could be seen as a place where cooperation, skills development and gender equality are key components. By this perspective, both the youth and women might view mining activity as an attractive job and the work environment as a safe, and this could go a long way in breaking the unequal gender balance that exists in most mines today.

Keywords: Mining activity; deep mining; human operators; intelligent deep mine; work environment; organizational activity.

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Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

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This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well. 

Keywords: Data mining technique, the decision support system, knowledge and decision rules.

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Authors: M. Jamali, F. Taghiyareh

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The problem of frequent itemset mining is considered in this paper. One new technique proposed to generate frequent patterns in large databases without time-consuming candidate generation. This technique is based on focusing on transaction instead of concentrating on itemset. This algorithm based on take intersection between one transaction and others transaction and the maximum shared items between transactions computed instead of creating itemset and computing their frequency. With applying real life transactions and some consumption is taken from real life data, the significant efficiency acquire from databases in generation association rules mining.

Keywords: Association rules, data mining, frequent patterns, shared itemset.

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Authors: Benjamin Kuester, Bjoern Eilert, Malte Stonis, Ludger Overmeyer

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Increasing quality requirements make reliable and effective quality management indispensable. This includes the complaint handling in which the 8D method is widely used. The 8D report as a written documentation of the 8D method is one of the key quality documents as it internally secures the quality standards and acts as a communication medium to the customer. In practice, however, the 8D report is mostly faulty and of poor quality. There is no quality control of 8D reports today. This paper describes the use of natural language processing for the automated evaluation of 8D reports. Based on semantic analysis and text-mining algorithms the presented system is able to uncover content and formal quality deficiencies and thus increases the quality of the complaint processing in the long term.

Keywords: 8D report, complaint management, evaluation system, text-mining.

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Authors: Renáta Iváncsy, Sándor Juhász

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Web usage mining has become a popular research area, as a huge amount of data is available online. These data can be used for several purposes, such as web personalization, web structure enhancement, web navigation prediction etc. However, the raw log files are not directly usable; they have to be preprocessed in order to transform them into a suitable format for different data mining tasks. One of the key issues in the preprocessing phase is to identify web users. Identifying users based on web log files is not a straightforward problem, thus various methods have been developed. There are several difficulties that have to be overcome, such as client side caching, changing and shared IP addresses and so on. This paper presents three different methods for identifying web users. Two of them are the most commonly used methods in web log mining systems, whereas the third on is our novel approach that uses a complex cookie-based method to identify web users. Furthermore we also take steps towards identifying the individuals behind the impersonal web users. To demonstrate the efficiency of the new method we developed an implementation called Web Activity Tracking (WAT) system that aims at a more precise distinction of web users based on log data. We present some statistical analysis created by the WAT on real data about the behavior of the Hungarian web users and a comprehensive analysis and comparison of the three methods

Keywords: Data preparation, Tracking individuals, Web useridentification, Web usage mining

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Authors: Hany Mahgoub

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This paper presents a system for discovering association rules from collections of unstructured documents called EART (Extract Association Rules from Text). The EART system treats texts only not images or figures. EART discovers association rules amongst keywords labeling the collection of textual documents. The main characteristic of EART is that the system integrates XML technology (to transform unstructured documents into structured documents) with Information Retrieval scheme (TF-IDF) and Data Mining technique for association rules extraction. EART depends on word feature to extract association rules. It consists of four phases: structure phase, index phase, text mining phase and visualization phase. Our work depends on the analysis of the keywords in the extracted association rules through the co-occurrence of the keywords in one sentence in the original text and the existing of the keywords in one sentence without co-occurrence. Experiments applied on a collection of scientific documents selected from MEDLINE that are related to the outbreak of H5N1 avian influenza virus.

Keywords: Association rules, information retrieval, knowledgediscovery in text, text mining.

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Authors: Ikmal Hisyam Mohamad Paris, Lilly Suriani Affendey, Norwati Mustapha

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In this paper we compare the accuracy of data mining methods to classifying students in order to predicting student-s class grade. These predictions are more useful for identifying weak students and assisting management to take remedial measures at early stages to produce excellent graduate that will graduate at least with second class upper. Firstly we examine single classifiers accuracy on our data set and choose the best one and then ensembles it with a weak classifier to produce simple voting method. We present results show that combining different classifiers outperformed other single classifiers for predicting student performance.

Keywords: Classification, Data Mining, Prediction, Combination of Multiple Classifiers.

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Authors: Abdellghani Bellaachia, Edward Jimenez

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Music Information Retrieval (MIR) and modern data mining techniques are applied to identify style markers in midi music for stylometric analysis and author attribution. Over 100 attributes are extracted from a library of 2830 songs then mined using supervised learning data mining techniques. Two attributes are identified that provide high informational gain. These attributes are then used as style markers to predict authorship. Using these style markers the authors are able to correctly distinguish songs written by the Beatles from those that were not with a precision and accuracy of over 98 per cent. The identification of these style markers as well as the architecture for this research provides a foundation for future research in musical stylometry.

Keywords: Music Information Retrieval, Music Data Mining, Stylometry.

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Authors: Debabrata Mazumder

Abstract:

Constructed and natural wetlands are being used extensively to treat different types of wastewater including the domestic one. Considerable removal efficiency has been achieved for a variety of pollutants like BOD, nitrogen and phosphorous in the wetlands. Wetland treatment appears to be the best choice for treatment or pre-treatment of wastewater because of the low maintenance cost and simplicity of operation. Wetlands are the natural exporters of organic carbon on account of decomposition of organic matter. The emergent plants like reeds, bulrushes and cattails are commonly used in constructed wetland for the treatment process providing surface for bacterial growth, filtration of solids, nutrient uptake and oxygenation to promote nitrification as well as denitrification. The present paper explored different scopes of organic matter (BOD), nitrogen and phosphorous removal from wastewater through wetlands. Emphasis is given to look into the soil chemistry for tracing the behavior of carbon, nitrogen and phosphorus in the wetland. Due consideration is also made to see the viability for upgrading the BOD, nitrogen and phosphorus removal efficiency through different classical modifications of wetland.

Keywords: BOD removal, modification, nitrogen removal, phosphorous removal, wetland.

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Authors: Sangdeok Lee, Sangwon Han, Changtaek Hyun

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Construction defects are major components that result in negative impacts on project performance including schedule delays and cost overruns. Since construction defects generally occur when a few associated causes combine, a thorough understanding of defect causality is required in order to more systematically prevent construction defects. To address this issue, this paper uses association rule mining (ARM) to quantify the causality between defect causes, and social network analysis (SNA) to find indirect causality among them. The suggested approach is validated with 350 defect instances from concrete works in 32 projects in Korea. The results show that the interrelationships revealed by the approach reflect the characteristics of the concrete task and the important causes that should be prevented.

Keywords: Causality, defect causes, social network analysis, association rule mining.

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Authors: M. Govindarajan, R. M.Chandrasekaran

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Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes proposed radial basis function Classifier that performs comparative crossvalidation for existing radial basis function Classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct Marketing. Direct marketing has become an important application field of data mining. Comparative Cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have high bias; its performance (accuracy estimation in our case) may be poor due to high variance. Thus the accuracy with proposed radial basis function Classifier was less than with the existing radial basis function Classifier. However there is smaller the improvement in runtime and larger improvement in precision and recall. In the proposed method Classification accuracy and prediction accuracy are determined where the prediction accuracy is comparatively high.

Keywords: Text Data Mining, Comparative Cross-validation, Radial Basis Function, runtime, accuracy.

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Authors: K. Soya, N. Mihara, D. Kuchar, M. Kubota, H. Matsuda, T. Fukuta

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The present work is concerned with sulfidation of Cu, Zn and Ni containing plating wastewater with CaS. The sulfidation experiments were carried out at a room temperature by adding solid CaS to simulated metal solution containing either single-metal of Ni, Zn and Cu, or Ni-Zn-Cu mixture. At first, the experiments were conducted without pH adjustment and it was found that the complete sulfidation of Zn and Ni was achieved at an equimolar ratio of CaS to a particular metal. However, in the case of Cu, a complete copper sulfidation was achieved at CaS to Cu molar ratio of about 2. In the case of the selective sulfidation, a simulated plating solution containing Cu, Zn and Ni at the concentration of 100 mg/dm3 was treated with CaS under various pH conditions. As a result, selective precipitation of metal sulfides was achieved by a sulfidation treatment at different pH values. Further, the precipitation agents of NaOH, Na2S and CaS were compared in terms of the average specific filtration resistance and compressibility coefficients of metal sulfide slurry. Consequently, based on the lowest filtration parameters of the produced metal sulfides, it was concluded that CaS was the most effective precipitation agent for separation and recovery of Cu, Zn and Ni.

Keywords: Calcium sulfide, Plating Wastewater, Filtrationcharacteristics, Heavy metals, Sulfidation.

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: Opinion Mining, Opinion Summarization, Sentiment Analysis, Text Mining.

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Authors: Nermin Ozgulbas, Ali Serhan Koyuncugil

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One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an Early Warning System (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7,853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.

Keywords: Early Warning Systems, Data Mining, Financial Risk, SMEs.

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Authors: Wimalin S. Laosiritaworn, Pongsak Holimchayachotikul

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this article proposed a methodology for computer numerical control (CNC) machine scoring. The case study company is a manufacturer of hard disk drive parts in Thailand. In this company, sample of parts manufactured from CNC machine are usually taken randomly for quality inspection. These inspection data were used to make a decision to shut down the machine if it has tendency to produce parts that are out of specification. Large amount of data are produced in this process and data mining could be very useful technique in analyzing them. In this research, data mining techniques were used to construct a machine scoring model called 'machine priority assessment model (MPAM)'. This model helps to ensure that the machine with higher risk of producing defective parts be inspected before those with lower risk. If the defective prone machine is identified sooner, defective part and rework could be reduced hence improving the overall productivity. The results showed that the proposed method can be successfully implemented and approximately 351,000 baht of opportunity cost could have saved in the case study company.

Keywords: Computer Numerical Control, Data Mining, HardDisk Drive.

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Authors: Aresh Vikram Singh, Sarika Nagar

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The tamarind based resin containing hydroxypropane sulphonic acid groups has been synthesized and their adsorption behavior for heavy metal ions has been investigated using batch and column experiments. The hydroxypropane sulphonic acid group has been incorporated onto tamarind by a modified Porath's method of functionalisation of polysaccharides. The tamarind hydroxypropane sulphonic acid (THPSA) resin can selectively remove of heavy metal ions, which are contained in industrial wastewater. The THPSA resin was characterized by FTIR and thermogravimetric analysis. The effects of various adsorption conditions, such as pH, treatment time and adsorbent dose were also investigated. The optimum adsorption condition was found at pH 6, 120 minutes of equilibrium time and 0.1 gram of resin dose. The orders of distribution coefficient values were determined.

Keywords: Distribution coefficient, industrial wastewater, polysaccharides, tamarind hydroxypropane sulphonic acid resin, thermogravimetric analysis.

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Authors: Ashutosh Kumar Choudhary, Satish Kumar, Chhaya Sharma

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This study evaluates the performance of horizontal subsurface flow constructed wetland (HSSF-CW) for the removal of chlorinated resin and fatty acids (RFAs) from pulp and paper mill wastewater. The dimensions of the treatment system were 3.5 m x 1.5 m x 0.28 m with surface area of 5.25 m2, filled with fine sand and gravel. The cell was planted with an ornamental plant species Canna indica. The removal efficiency of chlorinated RFAs was in the range of 92-96% at the hydraulic retention time (HRT) of 5.9 days. Plant biomass and soil (sand and gravel) were analyzed for chlorinated RFAs content. No chlorinated RFAs were detected in plant biomass but detected in soil samples. Mass balance studies of chlorinated RFAs in HSSF-CW were also carried out.

Keywords: Canna indica, Chlorinated resin & fatty acids, Constructed wetland, Pulp and paper mill wastewater.

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Authors: Wen-Tsann Lin, Yung-Tsan Jou, Yih-Chuan Wu, Yuan-Du Hsiao

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The Emergency Department of a medical center in Taiwan cooperated to conduct the research. A predictive model of triage system is contracted from the contract procedure, selection of parameters to sample screening. 2,000 pieces of data needed for the patients is chosen randomly by the computer. After three categorizations of data mining (Multi-group Discriminant Analysis, Multinomial Logistic Regression, Back-propagation Neural Networks), it is found that Back-propagation Neural Networks can best distinguish the patients- extent of emergency, and the accuracy rate can reach to as high as 95.1%. The Back-propagation Neural Networks that has the highest accuracy rate is simulated into the triage acuity expert system in this research. Data mining applied to the predictive model of the triage acuity expert system can be updated regularly for both the improvement of the system and for education training, and will not be affected by subjective factors.

Keywords: Back-propagation Neural Networks, Data Mining, Emergency Department, Triage System.

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Authors: E. A. Kiridi, A. O. Ogunlela

Abstract:

Conventional wastewater treatment plants of activated carbon, electrodialysis, ion exchange, reverse osmosis etc. are expensive to install, operate and maintain especially in developing countries; therefore, the use of aquatic macrophytes for wastewater purification is a viable alternative. On the first day of experimentation, approximately 100g of water hyacinth was introduced into the hydroponic units in four replicates. The water quality parameters measured were total suspended solids (TSS), pH and electrical conductivity (EC). Others were concentration of ammonium–nitrogen (NH₄⁺-N), nitrite-nitrogen (NO₂^--N), nitrate-nitrogen (NO₃^--N), phosphate–phosphorus (PO₄^3--P), and biomass value. At phytoremediation intervals of 7, 14, 21 and 28 days, the biomass recorded were 438.2 g, 600.7 g, 688.2 g and 725.7 g. Water hyacinth was able to reduce the pollutant concentration of all the selected parameter. The percentage reduction of pH ranged from 1.9% to 14.7%, EC from 49.8% to 97.0%, TDS from 50.4% to 97.6%, TSS from 34.0% to 78.3%, NH₄⁺-N from 38.9% to 85.2%, NO₂^--N from 0% to 84.6%, NO₃^--N from 63.2% to 98.8% and PO₄^3--P from 10% to 88.0%. Paired sample t-test shows that at 95% confidence level, it can be concluded statistically that the inequality between the pre-treatment and post-treatment values are significant. This suggests that the use of water hyacinth is valuable in the design and operation of aquaculture effluent treatment and should therefore be adopted by environmental and wastewater managers.

Keywords: Aquaculture effluent, phytoremediation, pollutant, water hyacinth.

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Authors: Xu Du, Aparna S. Varde

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In this research, we propose to conduct diagnostic and predictive analysis about the key factors and consequences of urban population relocation. To achieve this goal, urban simulation models extract the urban development trends as land use change patterns from a variety of data sources. The results are treated as part of urban big data with other information such as population change and economic conditions. Multiple data mining methods are deployed on this data to analyze nonlinear relationships between parameters. The result determines the driving force of population relocation with respect to urban sprawl and urban sustainability and their related parameters. This work sets the stage for developing a comprehensive urban simulation model for catering to specific questions by targeted users. It contributes towards achieving sustainability as a whole.

Keywords: Data Mining, Environmental Modeling, Sustainability, Urban Planning.

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Authors: El Youssfi L., Choukr-Allah R., Zaafrani M., Mediouni T., Sarr F, Hirich A.

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An experiment was implemented in a filed in the south of Morocco to evaluate the effects of domestic treated wastewater use for irrigation of amaranth crop under semi-arid conditions. Three varieties (A0020, A0057 & A211) were tested and irrigated using domestic treated wastewater EC1 (0,92 dS/m) as control, EC3 (3dS/m) and EC6 (6dS/m) obtained by adding sea water. In term of growth, an increase of the EC level of applied irrigation water reduced significantly the plant-s height, leaf area, fresh and dry weight measured at vegetative, flowering and maturity stage for all varieties. Even with the application of the EC6, yields were relatively higher in comparison with the once obtained in normal cultivation conditions. A significant accumulation of nitrate, chloride and sodium in soil layers during the crop cycle was noted. The use of treated waste water for its irrigation is proved to be possible. The variety A211 had showed to be less sensitive to salinity stress and it could be more promising its introduction to study area.

Keywords: Amaranth, salinity, semi-arid, treated waste water.

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Authors: Elsayeda M. Elgaml, Dina M. Ibrahim, Elsayed A. Sallam

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Association rule mining is one of the most important fields of data mining and knowledge discovery. In this paper, we propose an efficient multiple support frequent pattern growth algorithm which we called “MSFP-growth” that enhancing the FPgrowth algorithm by making infrequent child node pruning step with multiple minimum support using maximum constrains. The algorithm is implemented, and it is compared with other common algorithms: Apriori-multiple minimum supports using maximum constraints and FP-growth. The experimental results show that the rule mining from the proposed algorithm are interesting and our algorithm achieved better performance than other algorithms without scarifying the accuracy. 

Keywords: Association Rules, FP-growth, Multiple minimum supports, Weka Tool

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Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

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This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, Apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: Behavior, data mining technique, Apriori algorithm.

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Authors: Ahmed Arkoazi, Hussein Znad, Ranjeet Utikar

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The synergistic impact and optimization of gas flow rate, concentration of CO₂, and light intensity on CO₂ biofixation rate were investigated using wastewater as a medium to cultivate Chlorella vulgaris under different conditions (gas flow rate 1-8 L/min), CO₂ concentration (0.03-7%), and light intensity (150-400 µmol/m².s)). Response Surface Methodology and Box-Behnken experimental Design were applied to find optimum values for gas flow rate, CO₂ concentration, and light intensity. The optimum values of the three independent variables (gas flow rate, concentration of CO₂, and light intensity) and desirability were 7.5 L/min, 3.5%, and 400 µmol/m².s, and 0.904, respectively. The highest amount of biomass produced and CO₂ biofixation rate at optimum conditions were 5.7 g/L, 1.23 gL^-1d^-1, respectively. The synergistic effect between gas flow rate and concentration of CO₂, and between gas flow rate and light intensity was significant on the three responses, while the effect between CO₂ concentration and light intensity was less significant on CO₂ biofixation rate. The results of this study could be highly helpful when using microalgae for CO₂ biofixation in wastewater treatment.

Keywords: Synergistic impact, optimization, CO2 biofixation, airlift reactor.

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Authors: Meng-Hui Chen, Chen-Yu Kao, Chia-Yu Hsu, Pei-Chann Chang

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The main idea in this paper is using sequential pattern mining to find the information which is helpful for finding high performance solutions. By combining this information, it is defined as blocks. Using the blocks to generate artificial chromosomes (ACs) could improve the structure of solutions. Estimation of Distribution Algorithms (EDAs) is adapted to solve the combinatorial problems. Nevertheless many of these approaches are advantageous for this application, but only some of them are used to enhance the efficiency of application. Generating ACs uses patterns and EDAs could increase the diversity. According to the experimental result, the algorithm which we proposed has a better performance to solve the permutation flow-shop problems.

Keywords: Combinatorial problems, Sequential Pattern Mining, Estimation of Distribution Algorithms, Artificial Chromosomes.

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Authors: A. A. Ghanim

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The Kingdom of Saudi Arabia (KSA) is heading towards a severe and rapidly expanding water crisis, which can have negative impacts on the country’s environment and economy. Of the total water consumption in KSA, the agricultural sector accounts for nearly 87% of the total water use and, therefore, any attempt that overlooks this sector will not help in improving the sustainability of the country’s water resources. KSA Vision 2030 gives priority of water use in the agriculture sector for the regions that have natural renewable water resources. It means that there is little concern for making reuse of municipal wastewater for irrigation purposes in any region in general and in water-scarce regions in particular. The use of treated wastewater is very limited in Saudi Arabia, but it has very considerable potential for future expansion due its numerous beneficial uses. This study reviews the current situation of water resources in Saudi Arabia, providing more highlights on agriculture and wastewater reuse. The reviewed study is proposing some corrective measures for development and better management of water resources in the Kingdom. Suggestions also include consideration of treated water as an alternative source for irrigation in some regions of the country. The study concluded that a sustainable solution for the water crisis in KSA requires implementation of multiple measures in an integrated manner. The integrated solution plan should focus on two main directions: first, improving the current management practices of the existing water resources; second, developing new water supplies from both conventional and non-conventional sources.

Keywords: Saudi Arabia, water resources, water crisis, treated wastewater.

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Authors: W. Mughees, M. Al-Ahmad, M. Naeem

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This research involves the design and analysis of pinch-based water/wastewater networks to minimize water utility in the petrochemical and petroleum industries. A study has been done on Tehran Oil Refinery to analyze feasibilities of regeneration, reuse and recycling of water network. COD is considered as a single key contaminant. Amount of freshwater was reduced about 149m3/h (43.8%) regarding COD. Re-design (or retrofitting) of water allocation in the networks was undertaken. The results were analyzed through graphical method and mathematical programming technique which clearly demonstrated that amount of required water would be determined by mass transfer of COD.

Keywords: Minimization, Water Pinch, Water Management, Pollution Prevention.

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