Assessment of electrocoagulation for the treatment of petroleum refinery wastewater

Batch electrocoagulation experiments were carried out to evaluate the removal of sulfate and COD from petroleum refinery wastewater using three types of electrodes: aluminum, stainless steel, and iron. The effects of current density, electrode arrangement, electrolysis time, initial pH, and temperature were investigated for two wastewater samples with different concentrations of COD and sulfate. The experimental results indicated that the utilization of aluminum, as anode and cathode, was by far the most efficient arrangement in the reduction of both the contaminants. The treatment process was found to be largely affected by the current density and the initial composition of the wastewater. Although electrocoagulation was found to be most effective at 25°C and a pH of 8, the influence of these two parameters on the removal rate was not significant. The results demonstrated the technical feasibility of electrocoagulation as a possible and reliable technique for the pretreatment of heavily contaminated petroleum refinery wastewater.     

Fate of pathogenic microorganisms and indicators in secondary activated sludge wastewater treatment plants

This study was undertaken to investigate the removal of pathogenic microorganisms and their indicators in a laboratory scale biological treatment system that simulated the secondary treatment process of a wastewater treatment plant (WWTP). Four groups of microorganisms including bacteria, viruses, protozoa and helminths as well as the selected indicators were employed in the investigation. The results demonstrated that approximately 2-3 log10 removal of the microbial indicators was achieved in the treatment process. The log removal of Clostridium perfringens spores was low due to their irreversible adsorption to sludge flocs. The laboratory treatment system demonstrated a similar removal capability for Escherichia coli and the bacterial indicators (total coliforms, enterococci and particles <2.73 microm/L). The MS-2 bacteriophage, measured as a viral indicator, showed a lower removal than poliovirus, which may be considered as a worst case scenario for virus removal. The results of using particle profiling as an indicator for protozoa and helminths appeared to be inaccurate. The removal performance for bacterial and protozoan pathogens and their indicators in a full scale WWTP and the laboratory treatment system was compared.     

A comparative study on cost analysis,efficiency, and process mechanism of effluent treatment plants in Bangladesh

Investigations were conducted into the treatment of effluents produced during manufacturing processes at both a chemicals production facility and a paint manufacturing facility. A comparison of costs of wastewater treatment at both facilities was also performed. The untreated effluents from both facilities were high in biological oxygen demand (BOD), chemical oxygen demand (COD), and total dissolved solids (TDS). In addition, the effluents from the two facilities deviated significantly in dissolved oxygen (DO) content and pH levels. However, both facilities ultimately released treated wastewater with allowable amounts or levels of BOD, COD, TDS, DO, and pH as permitted by the Department of the Environment, Ministry of Environment and Forests, Bangladesh (DOE). The effluent treatment plants (ETP) at both facilities contained combinations of chemical and biological treatment processes. The treatment processes used at the chemicals production facility and at the paint manufacturing facility were continuous and semi‐batch processes, respectively. The biological treatment section of the ETP at the chemicals production facility has both anaerobic and aerobic units, while the paint manufacturing facility has only an aerobic unit. Annual installation and operation costs of the ETP at the chemicals production facility was Bangladeshi Taka (Tk) 1,300,000 ($16,667 US dollars) and Tk 800,000 ($10,257), respectively. The annual installation and operation costs of the ETP at the paint manufacturing facility were Tk 3,050,000 ($39,103) and Tk 6,200,000 ($79,488), respectively.     

Performance and modeling studies of rotating biological contactor for treatment of vegetable oil wastewater

This study focuses on a lab-scale rotating biological contactor (RBC) treating vegetable oil wastewater with high BOD and COD. The fabricated RBC was checked for efficiency in degrading polluted wastewater under different operating conditions. The maximum removal efficiencies for BOD and COD were 95.75% and 89%, respectively. This high removal percentage was obtained with 30% submergence of 10 discs rotating at 8 rpm. For the first time, bio-kinetic models were applied to the experimental results for vegetable oil wastewater. The best fit was obtained with the modified Stover-Kincannon and Grau model. The saturation constant (K_s) values were 1.872 and 3.024 g/L/d for BOD and COD, respectively, for the modified Stover-Kincannon and Grau model. For the Grau second-order model, the saturation constant was 1.416 and 3.744 g/L/d for BOD and COD, respectively. The predicted effluent BOD and COD values of the modified Stover–Kincannon model fitted almost exactly with the experimental values. This clearly predicts that this model can be best used to predict effluent BOD and COD concentration in a Rotating Biological contactor treating vegetable oil wastewater. The kinetic parameters determined in this study can be used to improve the design and operation of continuous mode RBC systems.     

Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

The volcanic plate made pillar cooler system is designed for outdoor spaces as a heat exchanging medium and reduces the outcoming air temperature which flows through the exhaust port. This paper proposes the use of artificial neural networks (ANNs) to predict inside air temperature of a pillar cooler. For this purpose, at first, three statistically significant factors (outside temperature, airing and watering) influencing the inside air temperature of the pillar cooler are identified as input parameters for predicting the output (inside air temperature) and then an ANN was employed to predict the output. In addition, 70%, 15% and 15% data was chosen from a previously obtained data set during the field trial of the pillar cooler for training, testing and validation, respectively, and then an ANN was employed to predict inside air temperature. The training (0.99918), testing (0.99799) and validation errors (0.99432) obtained from the model indicate that the artificial neural network model (NARX) may be used to predict inside air temperature of pillar cooler. This study reveals that, an ANN approach can be used successfully for predicting the performance of pillar cooler.     

Precipitation assessment in wastewater treatment plants operated for biological nutrient removal: a case study in Murcia, Spain

The Murcia Este Wastewater Treatment Plant is the largest wastewater treatment plant in Murcia (Spain). The plant operators have continuously found pipe blockage and accumulation of solids on equipment surfaces during the anaerobic digestion and post-digestion processes. This work studies the precipitation problems in the Murcia Este Wastewater Treatment Plant in order to locate the sources of precipitation and its causes from an exhaustive mass balance analysis. The DAF thickener and anaerobic digester mass balances suggest that most of the polyphosphate is released during excess sludge thickening. Despite the high concentrations achieved in the thickened sludge, precipitation does not occur at this point due to the low pH. The increases in ammonium and pH during anaerobic digestion cause precipitation to take place mainly inside the digesters and in downstream processes. This study shows that 50.7% of the available phosphate is fixed in the digester of which 52.0% precipitates as ammonium struvite, 39.2% precipitates as hydroxyapatite and the remaining 8.8% is adsorbed on the surface of the solids. Thermodynamic calculations confirm the precipitation of struvite and hydroxyapatite and also confirm that potassium struvite does not precipitate in the anaerobic digesters.     

Application of strategies for sanitation management in wastewater treatment plants in order to control/reduce greenhouse gas emissions

Greenhouse gases (GHG), basically methane (CH(4)), carbon dioxide (CO(2)) and nitrous oxide (N(2)O), occur at atmospheric concentrations of ppbv to ppmv under natural conditions. GHG have long mean lifetimes and are an important factor for the mean temperature of the Earth. However, increasing anthropogenic emissions could produce a scenario of progressive and cumulative effects over time, causing a potential "global climate change". Biological degradation of the organic matter present in wastewater is considered one of the anthropogenic sources of GHG. In this study, GHG emissions for the period 1990-2027 were estimated considering the sanitation process and the official domestic wastewater treatment startup schedule approved for the Metropolitan Region (MR) of Santiago, Chile. The methodology considers selected models proposed by the Intergovernmental Panel on Climate Change (IPCC) and some others published by different authors; these were modified according to national conditions and different sanitation and temporal scenarios. For the end of the modeled period (2027), results show emissions of about 65Tg CO(2) equiv./year (as global warming potential), which represent around 50% of national emissions. These values could be reduced if certain sanitation management strategies were introduced in the environmental management by the sanitation company in charge of wastewater treatment.     

Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model to improve the accuracy of the biological oxygen demand (BOD) estimation. Many of the water quality variables (chemical oxygen demand, temperature, dissolved oxygen, water flow, chlorophyll a and nutrients, ammonia, nitrite, nitrate) that affect biological oxygen demand concentrations were collected at 11 sampling sites in the Melen River Basin during 2001-2002. To develop an ANN model for estimating BOD, the available data set was partitioned into a training set and a test set according to station. In order to reach an optimum amount of hidden layer nodes, nodes 2, 3, 5, 10 were tested. Within this range, the ANN architecture having 8 inputs and 1 hidden layer with 3 nodes gives the best choice. Comparison of results reveals that the ANN model gives reasonable estimates for the BOD prediction.     

Reuse of wastewater for industrial needs: the Pontedera case

This paper presents result from an economic and technical point of view evaluation study, which has sought to establish the potential for replacing well-water that is currently used at the Piaggio industrial plant in Pontedera with recycled wastewater. The study is based upon a pilot wastewater treatment plant, which has been used to test wastewater treatment processes and to compare with achieved quality of recycled waters with that of water drawn on-site from physical/chemical pre-treatment and end with a reverse osmosis and air stripping stage. However, after the physical/chemical pre-treatment, the first option uses oxidation with ozone followed by activated carbon filtration, whereas the second method uses ultrafiltration. The first method is more effective at reducing chemical oxygen demand (COD) whereas the second method is more effective in removing bacteria. Nonetheless, both methods deliver water for re-use of a quality comparable with or better than that of well-water. In principle, the reverse osmosis permeate from either process could be re-used in all production phases at the industrial plant. However, the method base upon oxidation with ozone followed by activated carbon filtration is preferred, because, the process is simpler and continuous. The economic analysis of this process gives an estimated cost of Euro 0.55 per m³ for treated water. This compares favorably with other treatment processes and with the cost of other forms of water supply, which are projected to increase in any case.     

Preliminary study of physico-chemical treatment options for hospital wastewater

Hospital effluents are loaded with pathogenic microorganisms, partially metabolized pharmaceutical substances, radioactive elements, and other toxic substances. Such effluents if not treated properly can damage the natural environment and create a biological imbalance. This paper points out the areas of concern for hospital wastewater disposal and reports the findings of a limited physico-chemical study of treatment options for hospital effluents conducted at Christian Medical College and Hospital, Vellore, Tamil Nadu. The effluent collected was checked for conventional parameters and subjected to coagulation experiments. The raw and settled effluents were coagulated with FeCl(3), filtered and disinfected. Physico-chemical treatment seems to be an attractive option for the cost-effective disposal of hospital effluents. The results of this study call for further detailed study in this area.     

Environmental and economic feasibility study of a total wastewater treatment network system

The synthesis of distributed wastewater treatment plants (WTPs) has been studied to reduce capital and operating costs associated with wastewater treatment. In this study, the environmental and economic feasibility of a total wastewater treatment network system (TWTNS) including distributed and terminal WTPs was estimated using life cycle assessment (LCA) and life cycle costing (LCC) methods. Wastewater sources and existing distributed and terminal WTPs in an iron and steel plant were networked. The TWTNS was generated from the optimal solution to a mathematical optimization model and compared to a conventional wastewater treatment system (CWTS). The environmental effect scores of the TWTNS were from 29.6% to 68.3% higher than those of the CWTS because of higher electricity consumption required to pump wastewater to the networked WTPs. However, the life cycle cost of the TWTNS was lower than that of the CWTS by 10.1% because of the decrease of the labor cost resulting from the closing of three distributed WTPs. Overall, the TWTNS was no more eco-efficient than the CWTS because the increase of environmental burdens outweighed the decrease of economic costs.     

Volatile organic compound emissions from wastewater treatment plants in Taiwan: legal regulations and costs of control

This study assessed volatile organic compound (VOC) emission characteristics from wastewater treatment plants (WWTPs) in five Taiwanese industrial districts engaged in numerous manufacturing processes, including petrochemical, science-based industry (primarily semiconductors, photo-electronics, electronic products and biological technology), as well as multiple manufacturing processes (primarily pharmaceuticals and paint manufacturing). The most aqueous hydrocarbons dissolved in the wastewater of Taiwanese WWTPs were acetone, acrylonitrile, methylene chloride, and chloroform for the petrochemical districts; acetone, chloroform, and toluene for the science-based districts; and chlorinated and aromatic hydrocarbons for the multiple industrial districts. The aqueous pollutants in the united WWTPs were closely related to the characteristics of the manufacturing plants in the districts. To effectively prevent VOC emissions from the primary treatment section of petrochemical WWTPs, the updated regulations governing VOC emissions were issued by the Taiwanese Environmental Protection Administration in September 2005, legally mandating a seal cover system incorporating venting and air purification equipment. Cost analysis indicates that incinerators with regenerative heat recovery are optimal for treating high VOC concentrations, exceeding 10,000ppm as CH(4), from the oil separation basins. However, the emission concentrations, ranging from 100 to 1000ppm as CH(4) from the other primary treatment facilities and bio-treatment stages, should be collected and then injected into the biological oxidation basins via existing or new blowers. The additional capital and operating costs required to treat the VOC emissions of 1000ppm as CH(4) from primary treatment facilities are less than US$0.1 for perm(3) wastewater treatment capacity.     

Environmental impact minimization of a total wastewater treatment network system from a life cycle perspective

Synthesis of distributed wastewater treatment plants (WTPs) has focused on cost reduction, but never on the reduction of environmental impacts. A mathematical optimization model was developed in this study to synthesize existing distributed and terminal WTPs into an environmentally friendly total wastewater treatment network system (TWTNS) from a life cycle perspective. Life cycle assessment (LCA) was performed to evaluate the environmental impacts of principal contributors in a TWTNS. The LCA results were integrated into the objective function of the model. The mass balances were formulated from the superstructure model, and the constraints were formulated to reflect real wastewater treatment situations in industrial plants. A case study validated the model and demonstrated the effect of the objective function on the configuration and environmental performance of a TWTNS. This model can be used to minimize environmental impacts of a TWTNS in retrofitting existing WTPs in line with cleaner production and sustainable development.     

Cost-benefit analysis of water-reuse projects for environmental purposes: a case study for Spanish wastewater treatment plants

Water reuse is an emerging and promising non-conventional water resource. Feasibility studies are essential tools in the decision making process for the implementation of water-reuse projects. However, the methods used to assess economic feasibility tend to focus on internal costs, while external impacts are relegated to unsubstantiated statements about the advantages of water reuse. Using the concept of shadow prices for undesirable outputs of water reclamation, the current study developed a theoretical methodology to assess internal and external economic impacts. The proposed methodological approach is applied to 13 wastewater treatment plants in the Valencia region of Spain that reuse effluent for environmental purposes. Internal benefit analyses indicated that only a proportion of projects were economically viable, while when external benefits are incorporated all projects were economically viable. In conclusion, the economic feasibility assessments of water-reuse projects should quantitatively evaluate economic, environmental and resource availability.     

Determination of optimum operating conditions for industrial dye wastewater treatment using adaptive heuristic criticism pH control

For a pilot-scale application, pH control in the treatment of highly contaminated dye industrial wastewater containing metallic compounds as the main pollutants has been investigated with a method using adaptive heuristic criticism control (AHCC). Subsequent experimentation on between 12 and 18 l of the wastewater was carried out using statistical experimental design methodology to evaluate the effects of three critical factors: slaked lime (calcium hydroxide, Ca(OH)(2)) concentration, iron chloride (FeCl(3)) concentration and wastewater volume. With these critical factors, the wastewater treatment process is modeled as an appropriate quadratic cost function of the turbidity of the clarified water. The model is optimized with Rosenbrock's method. Response surface topology of the wastewater treatment is given in terms of optimal concentrations of lime water and FeCl(3) and optimal wastewater volume at pH 11.     

A comparative analysis of methods to represent uncertainty in estimating the cost of constructing wastewater treatment plants

Prediction of construction cost of wastewater treatment facilities could be influential for the economic feasibility of various levels of water pollution control programs. However, construction cost estimation is difficult to precisely evaluate in an uncertain environment and measured quantities are always burdened with different types of cost structures. Therefore, an understanding of the previous development of wastewater treatment plants and of the related construction cost structures of those facilities becomes essential for dealing with an effective regional water pollution control program. But deviations between the observed values and the estimated values are supposed to be due to measurement errors only in the conventional regression models. The inherent uncertainties of the underlying cost structure, where the human estimation is influential, are rarely explored. This paper is designed to recast a well-known problem of construction cost estimation for both domestic and industrial wastewater treatment plants via a comparative framework. Comparisons were made for three technologies of regression analyses, including the conventional least squares regression method, the fuzzy linear regression method, and the newly derived fuzzy goal regression method. The case study, incorporating a complete database with 48 domestic wastewater treatment plants and 29 industrial wastewater treatment plants being collected in Taiwan, implements such a cost estimation procedure in an uncertain environment. Given that the fuzzy structure in regression estimation may account for the inherent human complexity in cost estimation, the fuzzy goal regression method does exhibit more robust results in terms of some criteria. Moderate economy of scale exists in constructing both the domestic and industrial wastewater treatment plants. Findings indicate that the optimal size of a domestic wastewater treatment plant is approximately equivalent to 15,000 m3/day (CMD) and higher in Taiwan. Yet the optimal size of an industrial wastewater treatment plant could fall in between 6000 CMD and 20,000 CMD.     

Optimization of thermal performance of the parabolic trough solar collector systems based on GA-BP neural network model

The aim of this paper is to optimize the thermal performance (system output energy, thermal efficiency, and heat loss of cavity absorber) of parabolic trough solar collector (PTC) systems in order to improve its thermal performance, based on the genetic algorithm-back propagation (GA-BP) neural network model. There are a number of undefined problems, fuzzy or incomplete information and a complex thermal performance of the PTC systems. Therefore, the thermal performance prediction of the PTC systems based on GA-BP neural network model was developed. Subsequently, the metrics performances have been adopted to comprehensively understand the algorithm and evaluate the prediction accuracy. Results revealed that the GA-BP neural network model can be successfully used to predict the complex nonlinear relationship between the input variables and thermal performance of the PTC systems. The cosine effect has a great influence on the thermal performance; thereby the geometrical structure of the PTC systems was optimized. It was found that the optimized geometrical structure was beneficial to improve the thermal performance of the PTC system. In conclusion, the GA-BP neural network model has higher prediction accuracy than the other algorithm and it can be feasible and reliable.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

Analyzing the factors affecting environmental risks of projects using a hybrid approach of DEMATEL‐ANP,artificial neural network: A case study

The increasing growth of the economy in each country necessitates a great amount of investment in infrastructure. The belief that projects involve various uncertainties, such as technical skills, management quality, and the like, indicates that most projects fail to achieve their aims, interests, costs, as well as their timeframes and space requirements. As the environment can pose significant uncertainty to any project, environmental risks should be deeply studied by project management departments. This study intends to analyze as a case the environmental risk management system within a consulting firm. From this analysis, each aspect of a project's environmental risk management is ranked using a fuzzy analytical network process (ANP), a neural network algorithm, and a decision‐making trial and evaluation laboratory (DEMATEL) methodology. From the organizational aspect, budget risk is the most significant. From the technical aspect, the risk of regulations is the most important one. Finally, the risk of project failure from poor communication is another identified main risk in this research. By studying high‐ranking items in this hierarchy, it can be understood that these criteria exist in different aspects; therefore, all aspects of the risk should be taken into account to cover and assess risk. A neural network algorithm for validating and reassessment of ranking is employed. Results of this application showed that, based on Spearman's rank correlation method, two different approaches resulted in similar rankings. Finally, some practical implications for responding to the most highly ranked risks are proposed.     

Use of spent substrate after Pleurotus pulmonarius cultivation for the treatment of chlorothalonil containing wastewater

Lignocellulosic materials are used as substrate for the cultivation of the edible mushroom Pleurotus pulmonarius. After two or three flushes of mushrooms, the spent substrate is discarded although it still has an important enzymatic activity that can be used for several purposes. In this study, we sought to determine the technical feasibility of using spent substrate from P. pulmonarius to degrade chlorothalonil. Reaction mixture was prepared with 6 ml of pesticide aqueous solution (2 mg active ingredient/l) and 3 ml of enzymatic extract obtained from spent P. pulmonarius substrate. The enzymatic reaction (27 °C, pH 7.4) was conducted for 1 h with sampling at 15 min intervals. The effect of storage time and temperature (freezing or refrigerating) of spent substrate and enzymatic extract, respectively, on the activity over chlorothalonil was determined. Freshly obtained spent substrate extract was able to reduce 100% of the initial concentration of chlorothalonil (2 mg/l) after 45 min of reaction. Storage time had a negative effect on the stability of the enzymatic activity: with spent substrate stored for a week, chlorothalonil concentration was reduced in 49.5% after 1 h reaction and with substrate stored for two and three weeks, the degradation efficiency decreased to 9.15% and 0%, respectively. Cooling and freezing the spent substrate extract also had a negative effect on chlorothalonil degradation.