Screening of electrocoagulation process parameters for treated palm oil mill effluent using minimum-runs resolution IV design

The present study aimed at the screening of parameters for electrocoagulation treatment of treated palm oil mill efuent using minimum-runs resolution IV design. The responses examined include: chemical oxygen demand removal (%), total suspended solids removal (%) and turbidity reduction (%), and the varied dependent factors comprise: electrical current density (mA/cm2), time (min), pH, electrolyte concentration (g/L), stirring speed (rpm), electrode spacing (mm) and electrode confguration (monopolar or dipolar). The statistical results revealed that the current density has a signifcant infuence on the treatment performance at two-level interactions with pH, electrode spacing and electrode concentration and at three-level correlations with time and pH. Thus, the most important factors afecting the removal efciency of the organic compounds were found to be pH, time, electrode spacing, electrolyte concentration and electrode confguration at a P value less than 0.05, respectively, in the descending order of signifcance. Therefore, the optimized electrocoagulation process could be reached with current density equal to 5 mA/cm2, electrolysis time of 5 min, electrode spacing of 5 mm and using monopolar electrode confguration. This combination provided the maximum ability of the process for chemical oxygen demand (68.84%), total suspended solids (93.27%) and turbidity reduction (92.88%) predictions, with the corresponding experimental values of 69.27, 97.59 and 96.91%, respectively

Screening of electrocoagulation process parameters for treated palm oil mill effluent using minimun-runs resolution IV design

The present study aimed at the screening of parameters for electrocoagulation treatment of treated palm oil mill effluent using minimum-runs resolution IV design. The responses examined include: chemical oxygen demand removal (%), total suspended solids removal (%) and turbidity reduction (%), and the varied dependent factors comprise: electrical current density (mA/cm²), time (min), pH, electrolyte concentration (g/L), stirring speed (rpm), electrode spacing (mm) and electrode configuration (monopolar or dipolar). The statistical results revealed that the current density has a significant influence on the treatment performance at two-level interactions with pH, electrode spacing and electrode concentration and at three-level correlations with time and pH. Thus, the most important factors affecting the removal efficiency of the organic compounds were found to be pH, time, electrode spacing, electrolyte concentration and electrode configuration at a P value less than 0.05, respectively, in the descending order of significance. Therefore, the optimized electrocoagulation process could be reached with current density equal to 5 mA/cm², electrolysis time of 5 min, electrode spacing of 5 mm and using monopolar electrode configuration. This combination provided the maximum ability of the process for chemical oxygen demand (68.84%), total suspended solids (93.27%) and turbidity reduction (92.88%) predictions, with the corresponding experimental values of 69.27, 97.59 and 96.91%, respectively

Ammonical nitrogen effluent prediction using artificial neural network

Ammoniacal nitrogen (NH3-N) in domestic wastewater treatment plants (WWTP’s) has recently been added as the monitoring parameter by department of environment. It is necessary to obtain a suitable model for the prediction of NH3-N in the effluent stream of WWTP in order to meet the stringent environmental laws. Therefore, the study explores the robust capability of artificial neural network (ANN) in solving complex problems, as such similar to physical, chemical and biological environment of wastewater treatment plant. Data obtained from Bandar Tun Razak Sewerage Treatment Plant (STP) was used for development of the model. The prediction of ammoniacal nitrogen in the effluent stream using the developed model shows a satisfactory result for the reason that the mean square error (MSE) and correlation coefficient (R) were 0.1591 and 0.7980 respectively

Wastewater treatment plant performance evaluation using artificial neural network model

The final protection of wastewater pollutants to the environment is wastewater treatment plant (WWTP). This Research paper intends to develop a model from the past operational data of WWTP using artificial neural network (ANN) for the evaluation of the plant performance. The model was developed using four years’ operational data of Bandar Tun Razak sewerage treatment plant (STP) Kuala Lumpur, Malaysia. Matlab version 2008b software was used in training the ANN model. The model shows a high prediction capacity for multiple parallel outputs of effluent biochemical oxygen demand (BODe), chemical oxygen demand (CODe), suspended solid (SSe) and Ammonical nitrogen (NH3-Ne) than single output of each of the parameters individually. The coefficient of determination (R) and mean square error of the best model were 0.9449 and 2.86 respectively

Boutique effluent colour treatment using dead fungal biomass

The boutique industry has long become a cultural heritage of Malaysia, but the dyed effluents discharged into the water bodies were outrageous. This study assessed the potential of dead microorganism biosolids to treat the Boutique industry effluent. Therefore, the primary goal of this paper is to prepare an adsorbent using dead fungal biomass and evaluate its adsorption ability in removing colours from dye wastewater. The majority of a fungus strain in the wasted biomass used in producing the adsorbent is Phanerochaete chrysosporium. A feasibility study was done using Methylene-blue and Eriochrome black solutions as synthetic wastewater to examine the efficacy of the adsorbent. The optimization study was conducted on real boutique wastewater collected from Kelantan. Lastly, the colour removal efficiency as a function of process variables; biomass concentration, contact time, initial pH, and agitation speed was studied using five levels of central composite design and response surface methodology procedures. A good representable quadratic model was developed and optimized using the experimental data, and the best colour removal of about 93% was achieved according to the following process conditions; biomass concentration of 0.5 g/L, the processing time of 2 hours, initial pH of 8, and agitation speed of 200 rpm. The results demonstrated that the dead microorganism biomass would treat wastewater generated from the boutique industrie

Simulation of ammoniacal nitrogen effluent using feedforward multilayer neural networks

Ammoniacal nitrogen in domestic wastewater treatment plants has recently been added as the monitoring parameter by the Department of Environment, Malaysia. It is necessary to obtain a suitable model for the simulation of ammonical nitrogen in the effluent stream of sewage treatment plant in order to meet the new environmental laws. Therefore, this study explores the robust capability of artificial neural network in solving complex problems, which are similar to physical, chemical and biological conditions of wastewater treatment plant. Data obtained from Bandar Tun Razak Sewage Treatment plant was used for the model design. The simulation of ammoniacal nitrogen in the effluent stream by model shows a satisfactory result because the mean square error and correlation coefficients were 0.1591 and 0.7980, respectively

Screening of nutrients for the production of myco coagulant from lentinus squarrosulus for water treatment

Chemical coagulants have been continuously used for the treatment of water turbidity and suspended solids despite their harmful consequences on living beings. These problems obliged scientists in various parts of the world to investigate eco-friendly novel technologies. In this regard, a new Myco-coagulant is successfully produced by a group of researchers at the BioenvironmentalEngineering Research Centre of International Islamic University Malaysia. The present study assessed the nutrients level required to culture a previously isolated fungus from river water known as Lentinus squarrosulus. Synthetic water using kaolin suspension was prepared and used to study the removal of turbidity by the cultured Myco-coagulant using the flocculation mechanisms. Thus, the paper reported the experimental findings on nutrient screening and optimization of the suitable growth conditions and production of the fungus Myco-coagulant. The growth media that showed positive effects on the fungus growth according to Plackett–Burman design were yeast extract (11.2%), malt extract (6.0%), inoculum size (3.5%), and glucose (3.0%) 1However, agitation speed (-12.17%) had the most negative influence on Myco-coagulant growth, followed by urea (-7.67). Other growth conditions with negative effects includes; pH (-5.8%), culture time (-5.5%), and CaCl2 (-5.0%). Nevertheless, yeast extract and agitation speed were selected as the major parameters for fungus growth optimization. In conclusion, the submerged fermentation of Lentinus squarrosulus using yeast extract as a nutrient demonstrates a better yield 1than malt extract. Moreover, urea and CaCl2 could be excluded from the nutrient composition because of their insignificant contribution to fungal growth

Screening of nutrients for the productive of Myco-coagulant from Lentinus squarrosulus for water treatment

Chemical coagulants have been continuously used for the treatment of water turbidity and suspended solids despite their harmful consequences on living beings. These problems obliged scientists in various parts of the world to investigate eco-friendly novel technologies. In this regard, a new Myco-coagulant is successfully produced by a group of researchers at the Bioenvironmental Engineering Research Centre of International Islamic University Malaysia. The present study assessed the nutrients level required to culture a previously isolated fungus from river water known as Lentinus squarrosulus. Synthetic water using kaolin suspension was prepared and used to study the removal of turbidity by the cultured Myco-coagulant using the flocculation mechanisms. Thus, the paper reported the experimental findings on nutrient screening and optimization of the suitable growth conditions and production of the fungus Myco-coagulant. The growth media that showed positive effects on the fungus growth according to Plackett–Burman design were yeast extract (11.2%), malt extract (6.0%), inoculum size (3.5%), and glucose (3.0%). However, agitation speed (-12.17%) had the most negative influence on Myco-coagulant growth, followed by urea (-7.67). Other growth conditions with negative effects includes; pH (-5.8%), culture time (-5.5%), and CaCl2 (-5.0%). Nevertheless, yeast extract and agitation speed were selected as the major parameters for fungus growth optimization. In conclusion, the submerged fermentation of Lentinus squarrosulus using yeast extract as a nutrient demonstrates a better yield than malt extract. Moreover, urea and CaCl2 could be excluded from the nutrient composition because of their insignificant contribution to fungal growth