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Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. Two Well-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results.

In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BODeff), chemical oxygen demand (CODeff) and total nitrogen (TNeff). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods. The obtained results of single models proved that, ANFIS model provides effective outcomes in comparison with single models. In the ensemble modeling, simple averaging ensemble, weighted averaging ensemble and neural network ensemble techniques were proposed subsequently to improve the performance of the single models. The results showed that in prediction of BODeff, the ensemble models of simple averaging ensemble (SAE), weighted averaging ensemble (WAE) and neural network ensemble (NNE), increased the performance efficiency of artificial intelligence (AI) modeling up to 14%, 20% and 24% at verification phase, respectively, and less than or equal to 5% for both CODeff and TNeff in calibration phase. This shows that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel.

Industrial effluents are one of the foremost concerns relating to the anthropogenic environmental pollution. The effluents from the tanning and textile industries in Dhaka, Bangladesh, were characterized chemically and physicochemically with multivariate statistical techniques. The concentrations of heavy metals viz. , Pb, Cd, Cr, Mn, Fe, Ni, Cu, and Zn were determined by atomic absorption spectrometer while concentrations of anions viz. , F − , Cl − , NO 2 − , NO 3 − , and SO 4 2− were measured by ion chromatograph. The physicochemical parameters viz. , temperature, pH, electrical conductivity (EC), salinity, turbidity, dissolved oxygen (DO), and biological oxygen demand (BOD) were measured by a multiparameter meter while total suspended solids (TSS) and total dissolved solids (TDS) were measured gravimetrically. This study showed that effluents from both industries demonstrated high levels of TSS, TDS, EC, and heavy metals. Tannery effluents have lower pH and DO, and higher BOD, Cl − , SO 4 2− , and Cr concentrations while textile dyeing effluents have higher pH, NO 2 − , and NO 3 − concentrations, compared to the standard limits promulgated by the Bangladesh government. Multivariate statistical techniques such as cluster analysis and principal component analysis along with the correlation matrices showed significant association among the measured parameters and identified pollution sources as well as effluent types in the study area which could be linked to the processes used in textile dying and tanning industries. This study will be useful for identifying pollutants emanating from the two industries and will guide future industrial aquatic studies where multiple industrial runoffs are concerned.

In this study, observations were carried out in the surface waters of Saraydüzü Dam Lake within Sinop provincial borders for 1 year to determine water quality. The basic 28 variables used to determine water quality were measured monthly at six stations. Taking into account the World Health Organization's drinking water standards, the water quality index (WQI) and Turkey’s Ministry of Forestry and Water Affairs Surface Water Quality Regulations (SWQR) were used in determining the water quality. In addition, irrigation water quality was examined. For this, sodium absorption rates (SAR), sodium percentage and residual sodium carbonate (RSC) values were calculated. WQI values in the lake were found to be between 17.62 and 29.88. Water quality parameters did not exceed the recommended limit values in all months and at all stations. According to these values, the Saraydüzü Dam Lake water belongs to the ‘very good’ class in terms of drinking water quality. The results obtained showed that there were no nitrogen or phosphate inputs that could harm the ecosystem in the lake and that there were no low/insufficient ambient oxygen conditions resulting from excessive oxygen consumption during the degradation process of organic matter. All water quality parametres are well below the permissible limits except some heavy metals according to SWQR. Cu, Zn and Fe were found to exceed the limit values. The water quality of irrigation water was found to be good in terms of SAR and sodium percentage, whereas RSC was observed to have varying qualities during the year and not be suitable for irrigation in some months. According to results of factor analysis (FA), pH, temperature, electrical conductivity, suspended solid matter (SSM), biological oxygen demand (BOD), total hardness (TH),total alkalinity (TA), calcium, nitrate, ammonium, mercury and dissolved oxygen are the main variables responsible for the processes in the ecosystem.

The measurement of the wastewater BOD5 level requires five days, and the use of a prediction model to estimate BOD­5 saves time and enables the adoption of an online control system. This study investigates the application of artificial neural networks (ANNs) in predicting the influent BOD5 concentration and the performance of WWTPs. The WWTP performance was defined in terms of the COD, BOD, and TSS concentrations in the effluent. Sensitivity analysis was performed to identify the best-performing ANN network structure and configuration. The results showed that the ANN model developed to predict the BOD concentration performed the best among the three outputs. The top-performing ANN models yielded R2 values of 0.752, 0.612, and 0.631 for the prediction of the BOD, COD, and TSS concentrations, respectively. The optimal performing models were obtained (three inputs – one output), which indicated that the influent temperature and conductivity greatly affect the WWTP performance as inputs in all models. The developed prediction model for the influent BOD5 concentration attained a high accuracy, i.e., R2 = 0.754, which implies that the model is viable as a soft sensor for online control and management systems for WWTPs. Overall, the ANN model provides a simple approach for the prediction of the complex processes of WWTPs.

Landfill leachates from municipal landfills are usually heavily contaminated and thus require treatments before direct discharge into natural waters. Selecting the appropriate technology for leachate treatment is still a major challenge for operations in municipal landfills. Biodegradation is effective for treating young leachates, whereas old leachates require processes such as chemical oxidation, coagulation–flocculation, chemical precipitation, ozonation, activated carbon adsorption, and reverse osmosis. Recently, the combination of biological pretreatments followed by physico-chemical processes has been shown to be very efficient. Here we review the efficiency of biological treatment in combination with reverse osmosis to clean landfill leachates. We studied in particular processes including a membrane bioreactor, activated sludge, a rotating biological contactor, and up-flow anaerobic sludge blanket treatments, followed by reverse osmosis. We found a 99–99.5% removal of the chemical oxygen demand (COD), and a 99–99.8% removal of N–NH4+ using reverse osmosis and activated sludge. Using reverse osmosis with a rotating biological contactor, we observed 99% removal of COD, biochemical oxygen demand and N–NH4+. The combination of reverse osmosis, activated sludge and rotating biological contactor removed 98–99.2% of Cl− and 99–99.7% of Pb. Total suspended solids are best removed, up to 99%, by either a combination of reverse osmosis with membrane bioreactor, or reverse osmosis with activated sludge.

Payatas landfill in Quezon City, Philippines, releases leachate to the Marikina River through a creek. Multivariate statistical techniques were applied to study temporal and spatial variations in water quality of a segment of the Marikina River. The data set included 12 physico-chemical parameters for five monitoring stations over a year. Cluster analysis grouped the monitoring stations into four clusters and identified January–May as dry season and June–September as wet season. Principal components analysis showed that three latent factors are responsible for the data set explaining 83% of its total variance. The chemical oxygen demand, biochemical oxygen demand, total dissolved solids, Cl− and PO43− are influenced by anthropogenic impact/eutrophication pollution from point sources. Total suspended solids, turbidity and SO42− are influenced by rain and soil erosion. The highest state of pollution is at the Payatas creek outfall from March to May, whereas at downstream stations it is in May. The current study indicates that the river monitoring requires only four stations, nine water quality parameters and testing over three specific months of the year. The findings of this study imply that Payatas landfill requires a proper leachate collection and treatment system to reduce its impact on the Marikina River.

In the fast-changing world with increased water demand, water pollution, environmental problems, and related data, information on water quality and suitability for any purpose should be prompt and reliable. Traditional approaches often fail in the attempt to predict water quality classes and new ones are needed to handle a large amount or missing data to predict water quality in real time. One of such approaches is machine-learning (ML) based prediction. This paper presents the results of the application of the Naïve Bayes, a widely used ML method, in creating the prediction model. The proposed model is based on nine water quality parameters: temperature, pH value, electrical conductivity, oxygen saturation, biological oxygen demand, suspended solids, nitrogen oxides, orthophosphates, and ammonium. It is created in Netica software and tested and verified using data covering the period 2013–2019 from five locations in Vojvodina Province, Serbia. Forty-eight samples were used to train the model. Once trained, the Naïve Bayes model correctly predicted the class of water sample in 64 out of 68 cases, including cases with missing data. This recommends it as a trustful tool in the transition from traditional to digital water management.

An enhanced and different method for the active coagulant agent extraction from Moringa Oleifera seeds powder (MOSP) was established and compared to the conventional extraction method in distillate water. In the improved method, MOSP were extracted using sodium chloride as solvent at different concentrations to extract more coagulant agent from Moringa Oleifera and enhance coagulation activity. In this study, MOSP were initially processed and oil content was removed to minimize coagulant concentration usage (MOSP-EO). Moringa Oleifera seeds powder was characterized by both X-ray and FTIR analysis. Ultrasound treatment as well was considered as an additional treatment for MOSP-EO to investigate its effect on coagulant agent extraction process improvement. Coagulation/flocculation experiments were conducted to assess coagulant extraction performance realized through various conditions. The effect of coagulant dosage, solvent concentration and ultrasound exposition duration were investigated for a real effluent of municipal wastewater treatment. Among the three studied NaCl concentrations, 1.0 M was found to be the best solvent concentration for high turbidity removal of more than 97% using 140 mg/L of MOSP-EO compared to extraction in distillate water 88% using 170 mg/L of the same coagulant. NaCl 1.0 M demonstrated the best performance in biochemical oxygen demand (BOD5) removal as well, where more than 98% of municipal wastewater initial BOD5 was eliminated. Mixing MOSP-EO assisted with ultrasound waves at different treatment periods did decrease the active coagulant agent extraction and thus showed its inconvenient for Moringa Oleifera coagulation activity usage.

The Litany River has encountered severe environmental pollution. This study focused on assessing the pollution level in the upper basin of the Litany River by monitoring seasonal variation of water quality and testing physicochemical parameters and microbial qualities. A total of 72 freshwater samples were taken from six sites for 1 year during the four seasons. The microbiological parameters included total coliform, fecal coliform, and Escherichia coli counts. The physicochemical parameters comprised pH, total dissolved solids, nitrate, sodium, potassium, biochemical oxygen demand, chemical oxygen demand, total nitrogen, and total phosphorus. The microbiological quality of samples was evaluated by comparing the fecal pollution indicators loads to the SEQ-EAUX2003 standard for irrigation, and the physicochemical analyses were assessed according to Lebanese Standards Institution (LIBNOR) NL161: 2016 and the World Health Organization (WHO) Guidelines for Water Quality. The results revealed that most physicochemical parameters are not within the permitted limit of LIBNOR and WHO, especially in sites S2, S3, and S6 during the dry seasons. The pH ranged between 6 and 8.16. The total dissolved solids reached 1948 mg/L. The nitrate, sodium, and potassium ranged between 0 and 253 mg/L. The total nitrogen and total phosphorous reached 103 and 5.16 mg/L, respectively. The chemical oxygen demand reached 2210 mg/L, and the biochemical oxygen demand reached 732 mg/L. Concerning the microbiological analysis, fecal pollution was detected in all sites during all seasons, with detectable higher values during the dry seasons, and all samples were considered to be non-conforming, with significant spatiotemporal variation of most parameters. Our results highlight the need to take measures to prevent the high level of pollution. This could be achieved by monthly water quality monitoring of the upper basin and introducing appropriate guidelines to detect pathogens and toxic chemicals that affect the entire ecosystem and lead to severe public health issues.