The performance of integrated ultrasonic membrane anaerobic system (IUMAS) in treating sugar cane wastewater

Sugarcane mill effluent (SCME) causes severe environmental pollution due to its high concentration in term of pollutants. Conventional methods of treating SCME have disadvantages from both environmental and economic perspectives. Most of the treatment methods used the membrane as a solution to wastewater pollution problems but suffering from membrane fouling. In this study, the potentials of Integrated Ultrasonic Assisted Membrane Anaerobic System (IUMAS) in treating sugarcane mill effluent was investigated. In this research different organic loading rates were used as a fed to the system, which operated semi-continuously at mesophilic temperature 30°C to 35°C and pressure ranges of 1.5–2 bars. Seven steady states were accomplished as a part of a kinetic study that considered concentration ranges of 2500 mg/L to 6000 mg/L for mixed liquor suspended solids (MLSS). The aim was to obtain optimum operating conditions and maximum methane production as well as the performance of IUMAS comparing with membrane anaerobic system (MAS) in treating SCME. IUMAS depicted better performance as compared to MAS in treating the sugarcane mill effluent (SCME) as it achieved higher percentage removal efficiencies for COD, BOD, turbidity and TSS which were 96.12%, 67%, 94%and 98.8%, respectively. While higher percentage removal efficiencies for MAS were 93.8%, 66.3%, 73.8% and 97.4%. The highest methane percentage was 80.9 % for IUMAS compared with MAS was 77.3%. The SCME characterized to investigate by using a different analytical approach such as SEM/EDX, and FTIR. SEM morphology analysis for IUMAS, the permeate flux for the membrane filtration of SCME increased while for MAS decreased the permeate flux due to fouling problem. For FTIR in both methods obtained 5 identified peaks before treatment. However, after treatment indicated 6 and 5 identified peaks for IUMAS and MAS. Kinetic equations from Monod, Contois and Chen and Hashimoto were employed used IUMAS to describe the kinetics of SCME treatment. The correlation coefficient was 54% for Monod, 85% for Contois model and 91% for Chen and Hashimoto model. From the highest, R2 the best fitting in Chen and Hashimoto model. The growth yield coefficient Y and the specific microorganism decay rate b were determined as 0.23 g VSS/g COD and 0.0214 day-1 respectively. An optimization study for the preparation conditions of the selected optimum parameters for maximum methane gas was investigated using Response Surface Methodology (RSM). The determining factors such as pH, OLR, COD, and HRT were initially screened using 2 level factorial approach. The screening revealed that the effect of the above parameters was significant. Furthermore, the impact of these four operating parameters were investigated using the central composite design (CCD) techniques. The results presented the optimum conditions for methane yield from SCME were pH 7.1, OLR 8 kg COD/m3/day, COD HRT 5.65 day with CH4 84.7%. The results obtained in this study have exposed the capability of ultrasonic-assisted membrane anaerobic system (IUMAS) in treating SCME wastewater. Thus, this method can be a promising source for treating all industrial wastewater

Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso

The ability to create “structured models” of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, an Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm that can estimate the values of small-scale kinetic parameters is described and applied to E. coli’s main metabolic network as a model system. The glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate pathways, and acetate formation pathways of Escherichia coli are represented by the Differential Algebraic Equations (DAE) system for the metabolic network. However, this algorithm uses segments to organize particle movements and the dynamic inertia weight ((Formula presented.)) to increase the algorithm’s exploration and exploitation potential. As an alternative to the state-of-the-art algorithm, this adjustment improves estimation accuracy. The numerical findings indicate a good agreement between the observed and predicted data. In this regard, the result of the ESe-PSO algorithm achieved superior accuracy compared with the Segment Particle Swarm Optimization (Se-PSO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) algorithms. As a result of this innovative approach, it was concluded that small-scale and even entire cell kinetic model parameters can be developed

Estimation of Small-Scale Kinetic Parameters of <i>Escherichia coli</i> (<i>E. coli</i>) Model by Enhanced Segment Particle Swarm Optimization Algorithm ESe-PSO

The ability to create “structured models” of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, an Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm that can estimate the values of small-scale kinetic parameters is described and applied to E. coli’s main metabolic network as a model system. The glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate pathways, and acetate formation pathways of Escherichia coli are represented by the Differential Algebraic Equations (DAE) system for the metabolic network. However, this algorithm uses segments to organize particle movements and the dynamic inertia weight (ω) to increase the algorithm’s exploration and exploitation potential. As an alternative to the state-of-the-art algorithm, this adjustment improves estimation accuracy. The numerical findings indicate a good agreement between the observed and predicted data. In this regard, the result of the ESe-PSO algorithm achieved superior accuracy compared with the Segment Particle Swarm Optimization (Se-PSO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) algorithms. As a result of this innovative approach, it was concluded that small-scale and even entire cell kinetic model parameters can be developed