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This paper aims to evaluate the performance of the Mashhad No. 5 wastewater treatment plant (WWTP) using a combination of data mining (regression) algorithms and artificial neural networks.

In this research, the performance of WWTP located in Mashhad, Iran, has been evaluated using two data mining models, neural network and regression model.

The proposed model has the potential of implementing in other WWTPs in Iran or other countries.

The authors would also like to thank Mashhad No.5 WWTP for data access.

The Suez Irrigation Canal is the source of drinking water to a large community. Complaints have been raised regarding the odor and unpleasant taste of drinking water. The problems encountered reveled enrichment of the Canal with nutrients, degraded water quality and nuisance caused by algal growth. This paper aims to investigate these claims by evaluating the interaction between water and sediment with ecological indicators.

Bioassessments were used as a primary tool to evaluate the biological conditions and identify the degree of water quality degradation in the Suez Irrigation Canal. The monitoring program integrates biological, chemical, and physical data assessment. Several field surveys were carried out to these areas during the period between March 2003 and February 2005 (over 23 months) for acquiring all possible information about the current situation and to explore the impact of human activities along the canal banks on the canal ecosystem. Seasonal variations of phytoplankton and zooplankton standing crop, species diversity as well as physico‐chemical characteristics of water, sediment, fish and aquatic weeds at the intakes of drinking plants and from the discharge of agricultural and domestic drains into the Canal were investigated.

Preliminary field investigations showed great amounts of discharged wastes at several locations to the canal water creating unique conditions, which vary with changes of volume and properties of the discharged wastes. Rotifer and green algae for example demonstrated seasonal variable response to the ecological variations. Myriophyllum spicatum, Potamageton nodsus and Polygonum Salicfolium were the most common types of recorded weed. The Myriophyllum spicatum is the dominant submerged plant. The canal was characterized by high concentrations of HCO₃^‐ as well as high pH >8.2 which provides a favorable habitat for the growth of Myriophyllum spicatum. The results illustrated the ability of using the aquatic weed as biomarkers for monitoring heavy metals contaminates in the canal. The evidence suggests that there is a degree of selectivity in metals uptake and partitioning within the plant compartments.

The current paper adopts the idea of utilizing multiple organism groups in the bioassessment to effectively detect ecological change when they occur in one of the most important waterways in Egypt. These different organism groups are suited for detection various stressors, providing warnings and detection of stress impacts at different scales. The study presented provides decision makers with important information that can assist them in making objective decisions related to the design of monitoring programs based on scientific research.

Productivity improvement is key to sustainability performance improvements of organizations. In a real-world scenario, the nature of inputs and outputs is likely to be imprecise and vague, leading to complexity in comparing firms' efficiency measurements. Implementation of fuzzy-logic based measurement systems is a method for dealing with such cases. This paper presents a fuzzy weight objective function to solve Data Envelopment Analysis (DEA) CCR model for measuring paper mills' performance in India for 15 years.

An integrated methodology is proposed to solve DEA models having fuzzy weights. The fuzzy DEA methodology is an extended version of the DEA approach that researchers have used for performance measurement purposes in imprecise and vague scenarios. The ecological performance of the paper industry is evaluated, considering some desirable and undesirable outputs. The effect of non-discretionary input on the performance of a paper mill is also analyzed.

Analysis suggests that the productivity of the paper industry is improving consistently throughout the period. The comparative evaluation of methods suggests that a diverse cluster of DMUs and integration of DEA with the fuzzy logic increases the diversity in the efficiency score while DEA-DE imitates the results of CCR DEA.

Proposed a fuzzy DEA-based analytical framework for measuring the paper industry's ecological performance in an imprecise and vague scenario. The model is tested on data from the paper industry in a developing country context and comparative performance analysis using DEA, fuzzy DEA and DE algorithm is done.

Artificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks (ANNs) are one of the most important of these models, and they are increasingly being used to forecast water resource variables. The goal of this study was to create an ANN model to estimate the removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) at the effluent of various primary and secondary treatment methods in a wastewater treatment plant (WWTP).

The MATLAB App Designer model was used to generate the data set. Various combinations of wastewater quality data, such as temperature(T), TN, TP and hydraulic retention time (HRT) are used as inputs into the ANN to assess the degree of effect of each of these variables on BOD, TN, TP and TSS removal efficiency. Two of the models reflect two different types of primary treatment, while the other nine models represent different types of subsequent treatment. The ANN model’s findings are compared to the MATLAB App Designer model. For evaluating model performance, mean square error (MSE) and coefficient of determination statistics (R2) are utilized as comparative metrics.

For both training and testing, the R values for the ANN models were greater than 0.99. Based on the comparisons, it was discovered that the ANN model can be used to estimate the removal efficiency of BOD, TN, TP and TSS in WWTP and that the ANN model produces very similar and satisfying results to the APPDESIGNER model. The R-value (Correlation coefficient) of 0.9909 and the MSE of 5.962 indicate that the model is accurate. Because of the many benefits of the ANN models used in this study, it has a lot of potential as a general modeling tool for a range of other complicated process systems that are difficult to solve using conventional modeling techniques.

The objective of this study was to develop an ANN model that could be used to estimate the removal efficiency of pollutants such as BOD, TN, TP and TSS at the effluent of various primary and secondary treatment methods in a WWTP. In the future, the ANN could be used to design a new WWTP and forecast the removal efficiency of pollutants.

The purpose of this paper is to compare various landfill gas (LFG) and leachate treatment technologies in a life‐cycle perspective.

Since a landfill causes emissions for a very long‐time period, life‐cycle‐based environmental assessment was carried out to compare different technological options for sustainable leachate treatment and LFG collection and utilization. WAMPS, the life‐cycle assessment (LCA) model for waste management planning, was used for the environmental assessment of selected leachate and LFG treatment technologies.

Results of both direct measurements in the studied landfills and LCA support the fact that leachate treatment with reverse osmosis has the best environmental performance compared to aerobic‐activated sludge treatment. Recently, the collection efficiency of LFG in the studied landfills is relatively low. In order to improve the overall environmental performance of LFG management the gas collection rate should be improved. LFG utilisation for energy recovery is an essential part of the system. The results of the study show that the avoided impacts of energy recovery can be even greater than direct impacts of greenhouse gas emissions from landfills. Therefore, measures which combine LFG collection with energy generation should be preferred to treatment in flare.

It should be noted that the results of this study do not express the total environmental impacts of the entire landfill system, but only the eutrophicating impacts and global warming related to the studied leachate and LFG management options. Therefore, it is recommended that further LCAs investigate also other relevant impact categories.

The results of LCA modelling show that it is important to ensure the highest collection and treatment efficiency of leachate and LFG, since poor capture compromises the overall environmental performance of a landfill.

The paper provides a site‐specific data on sustainable leachate and LFG management in selected Estonian conventional municipal solid waste landfills. As such, the paper contributes to the development of the regional reference input data for LCA in waste management.

In this study, the oxidative degradation performance of indigo wastewater based on electrochemical systems was explored. The decolourization degrees, removal rate of chemical oxygen demand and biochemical oxygen demand of the indigo wastewater after degradation were evaluated and optimized treatment conditions being obtained.

The single factor method was first used to select the electrolyte system and electrode materials. Then the response surface analysis based on Box–Behnken Design was chosen to determine the influence of four independent variables such as FeCl₃ concentration, NaCl concentration, decolourization time and voltage on the degradation efficiency.

On the basis of single factor experiment, the electrode material of stainless steel was selected in the double cell, and the indigo wastewater was electrolyzed with FeCl₃ and NaCl electrolytes. The process conditions of electrochemical degradation of indigo wastewater were optimized by response surface analysis: the concentration of FeCl₃ and NaCl was of 16 and 9 g/L, respectively, with a decolourization time of 50 min, voltage of 10 V and decolourization percentage of 98.94. The maximum removal rate of chemical oxygen demand reached 75.46 per cent. The highest ratio of B/C was 3.77, which was considered to be more biodegradable.

Dyeing wastewater is bringing out more and more pollution problems to the environment. However, there are some shortcomings in traditional technologies such as adsorption and filtration. As a kind of efficient and clean water treatment technology, electrochemical oxidation has been applied to the treatments of various types of wastewater. The decolourization and degradation of indigo wastewater is taken as an example to provide reference for the treatment of wastewater in actual plants.

The developed method provided a simple and practical solution for efficiently degrading indigo wastewater.

The method for the electrochemical oxidation technology was novel and could find numerous applications in the degradation of printing and dyeing wastewater.

Based on the use of simple and relevant systems, several applications of UV spectrophotometry have recently been developed for the quality control of water, wastewater, air and contaminated soils for environmental, urban or industrial needs. These are outlined in this paper.

Astro Technology is offering the SVS 7400 and 7500 systems for automated inspection of solder paste and component placement, to ensure quality build and high yield in high‐throughput PCB assembly operations.

Environmental legislation and its enforcement has undoubtedly forced the textile industry to be rather cautious in selecting the appropriate processes and equipment. The most efficient, economic and minimal environmental pollution processing methods will be increasingly demanded throughout the 1990s. However, the majority of the textile industry consists of small and medium enterprises, where the lack of expertise on the use of best available techniques leads to levels of operation far away from the optimal. An expert system for a priori economic evaluation of potential interventions in the textile manufacturing processes and equipment is necessary to support decision making in the management level. This paper presents such an expert system, designed and implemented in four stages: formal description of the key factors that affect the dyeing process in the textile industry, development of models for the representation of relevant information, development of models for the representation of knowledge and integration of the above‐mentioned models in a unified information system that supports the decision‐making process in the management of textile enterprises.

The removal of Ismative SHR blue (reactive dye) using a very cheap cement factory waste called by‐pass kiln dust has been investigated in batch mode. The effects of initial dye solution concentration, particle size, agitation rate, amount of by‐pass kiln dust, and dye solution temperature were studied. A 95.5 per cent removal was achieved. Some pretreatment of by‐pass kiln dust, such as washing by distilled water or rinsing with HCL solution, has been done. By using the washed by‐pass kiln dust the values of total dissolved solids, alkalinity, and efficiency of colour removal were reduced. A very rapid irreversible chemical reaction may occur in addition to the adsorption process. Ferric chloride was used as a coagulant after settling to complete the separation of the dust from the clear solution. A removal of 96 per cent from the initial colour of the industrial coloured wastewater was achieved.