Optimization of culture conditions for biohydrogen production from sago wastewater by Enterobacter aerogenes using Response Surface Methodology

Sago wastewater (SWW) causes pollution to the environment due to its high organic content. Annually, about 2.5 million tons of SWW is produced in Malaysia. In this study, the potential of SWW as a substrate for biohydrogen production by Enterobacter aerogenes (E. aerogenes) was evaluated. Response Surface Methodology (RSM) was employed to find the optimum conditions. From preliminary optimization, it was found that the most significant factors were yeast extract, temperature, and inoculum size. According to Face Centered Central Composite Design (FCCCD), the maximum hydrogen concentration and yield were 630.67 μmol/L and 7.42 mmol H₂/mol glucose, respectively, which is obtained from the sample supplemented with 4.8 g/L yeast extract concentration, 5% inoculum, and incubated at the temperature of 31 °C. Cumulative hydrogen production curve fitted by the modified Gompertz equation suggested that H_max, R_max, and λ from this study were 15.10 mL, 2.18 mL/h, and 9.84 h, respectively.     

New approach to unsupported ReS2 nanorod catalyst for upgrading of heavy crude oil using methane as hydrogen source

Highly active ReS₂ nanocatalysts were prepared by CVD method and characterized by XRD, BET -BJH, Raman spectroscopy, XPS, TPR, NH₃-TPD, SEM, and HRTEM techniques. Catalytic activities were used in upgrading heavy crude oil using methane as hydrogen source. The results showed a significant increase in API and decrease in sulfur and nitrogen content of crude oil. RSM technique was used to investigate the interactive effects of temperature (200–400 °C), pressure (20–40 bar) and dosage of nanocatalyst (0.5–2 wt. %) on the performance of HDS reaction. The results represent that the maximum predicted HDS activity (74.375%) was estimated under the optimal conditions (400 °C, 20 bars, and 2 wt % of nanocatalyst). Also, the effect of reaction temperature, pressure and dosage of ReS₂ nanorods catalyst on HDN of heavy crude oil was investigated and highest efficiency in the HDN process (93%) occurred at 400 °C and 40 bar using 2 wt % ReS₂.     

Application of dielectric barrier discharge (DBD) plasma packed with glass and ceramic pellets for SO2 removal at ambient temperature: optimization and modeling using response surface methodology

Air pollution is a major health problem in developing countries and has adverse effects on human health and the environment. Non-thermal plasma is an effective air pollution treatment technology. In this research, the performance of a dielectric barrier discharge (DBD) plasma reactor packed with glass and ceramic pellets was evaluated in the removal of SO₂ as a major air pollutant from air in ambient temperature. The response surface methodology was used to evaluate the effect of three key parameters (concentration of gas, gas flow rate, and voltage) as well as their simultaneous effects and interactions on the SO₂ removal process. Reduced cubic models were derived to predict the SO₂ removal efficiency (RE) and energy yield (EY). Analysis of variance results showed that the packed-bed reactors (PBRs) studied were more energy efficient and had a high SO₂ RE which was at least four times more than that of the non-packed reactor. Moreover, the results showed that the performance of ceramic pellets was better than that of glass pellets in PBRs. This may be due to the porous surface of ceramic pellets which allows the formation of microdischarges in the fine cavities of a porous surface when placed in a plasma discharge zone. The maximum SO₂ RE and EY were obtained at 94% and 0.81 g kWh⁻¹, respectively under the optimal conditions of a concentration of gas of 750 ppm, a gas flow rate of 2 l min⁻¹, and a voltage of 18 kV, which were achieved by the DBD plasma packed with ceramic pellets. Finally, the results of the model's predictions and the experiments showed good agreement.     

Performance optimization of microbial electrolysis cell (MEC) for palm oil mill effluent (POME) wastewater treatment and sustainable Bio-H2 production using response surface methodology (RSM)

Microbial electrolysis cells (MECs) are a new bio-electrochemical method for converting organic matter to hydrogen gas (H₂). Palm oil mill effluent (POME) is hazardous wastewater that is mostly formed during the crude oil extraction process in the palm oil industry. In the present study, POME was used in the MEC system for hydrogen generation as a feasible treatment technology. To enhance biohydrogen generation from POME in the MEC, an empirical model was generated using response surface methodology (RSM). A central composite design (CCD) was utilized to perform twenty experimental runs of MEC given three important variables, namely incubation temperature, initial pH, and influent dilution rate. Experimental results from CCD showed that an average value of 1.16 m³ H₂/m³ d for maximum hydrogen production rate (HPR) was produced. A second-order polynomial model was adjusted to the experimental results from CCD. The regression model showed that the quadratic term of all variables tested had a highly significant effect (P < 0.01) on maximum HPR as a defined response. The analysis of the empirical model revealed that the optimal conditions for maximum HPR were incubation temperature, initial pH, and influent dilution rate of 30.23 ^°C, 6.63, and 50.71%, respectively. Generated regression model predicted a maximum HPR of 1.1659 m³ H₂/m³ d could be generated under optimum conditions. Confirmation experimentation was conducted in the optimal conditions determined. Experimental results of the validation test showed that a maximum HPR of 1.1747 m³ H₂/m³ d was produced.     

Optimization of a continuous ultrasound assisted oxidative desulfurization (UAOD) process of diesel using response surface methodology (RSM) considering operating cost

A continuous-flow ultrasound-assisted oxidative desulfurization(UAOD) of partially hydro-treated diesel has been investigated using hydrogen peroxide-formic acid as simple and easy to apply oxidation system. The effects of different operating parameters of oxidation stage including residence time(2–24 min), formic acid to sulfur molar ratio(10–150), and oxidant to sulfur molar ratio(5–35) on the sulfur removal have been studied using response surface methodology(RSM) based on Box–Behnken design. Considering the operating costs of the continuous-flow oxidation stage including chemical and electrical energy consumption, the appropriate values of operating parameters were selected as follows: residence time of 16 min, the formic acid to sulfur molar ratio of 54.47, and the oxidant to sulfur molar ratio of 8.24. In these conditions, the sulfur removal and the volume ratio of the hydrocarbon phase to the aqueous phase were 86.90% and 4.34, respectively. By drastic reduction in the chemical consumption in the oxidation stage, the volume ratio of the hydrocarbon phase to the aqueous phase was increased up to 10. Therefore, the formic acid to sulfur molar ratio and the oxidant to sulfur molar ratio were obtained 23.64 and 3.58, respectively, which lead to sulfur removal of 84.38% with considerable improvements on the operating cost of oxidation stage in comparison with the previous works.     

Optimization of sugar release from banana peel powder waste (BPPW) using box-behnken design (BBD): BPPW to biohydrogen conversion

Optimization of pre-treatment conditions has been achieved for total sugar release from banana peel powder waste (BPPW) feedstock modelled through a three-level Box-Behnken design (BBD) of the response surface methodology (RSM). A series of various runs were executed at varied acid (H₂SO₄) concentrations (0.05%–0.15% v/v), incubation periods (1 h–3 h) in water bath at 95 °C and alkali (NaOH) concentrations (0.05%–0.15% v/v) according to the Box-Behnken design (BBD). From RSM the significant values of incubation period, acid concentration and alkali concentration were obtained as 3 h, 0.095% v/v, and 0.05% v/v respectively. The maximum total sugar release was reported as 5243.62 μg/ml which was highly close to the predicted value (5010.07 μg/ml). The model P- value (0.001), R-sq (98.26%), (adj) R-sq (95.14%) and (pred) R-sq (79.56%) obtained through ANOVA justified the results. The mutual impact of alkali and incubation period had the highest effect on total sugar release from dried banana peel powder, followed by mutual impact of acid and incubation period based on ANOVA (Analysis of Variance) results.Under optimized conditions of pre-treatment six different substrate concentrations (1%, 3%, 5%, 7% and 9% w/v) of BPPW was hydrolyzed and used to obtain volumetric bio-hydrogen evolution. The highest cumulative volumetric bio hydrogen gas 43 ml H₂/30 ml media was achieved at 5% w/v of pretreated BPPW. The substrate concentration above 5% w/v resulted in lowered fermentation process owing to product and substrate inhibition.     

A new synthetic metamodel methodology for liquid‐propellant engine's cooling system optimization

The present paper strives for optimization of the cooling system of a liquid‐propellant engine (LPE). To this end, the new synthetic metamodel methodology utilizing the design of experiment method and the response surface method was developed and implemented as two effective means of designing, analyzing, and optimizing. The input variables, constraints, objective functions, and their surfaces were identified. Hence, the design and development strategy of combustion chamber and nozzle was clarified, and 64 different experiments were carried out on the RD‐161 propulsion system, of which 47 experiments were approved and compatible with the problem constraints. This engine used all three modes of cooling: the radiation cooling, the regenerative cooling, and the film cooling. The response surface curves were drawn and the related objective function equations were obtained. The analysis of variance results indicate that the developed synthetic model is capable to predict the responses adequately within the limits of input parameters. The three‐dimensional response surface curves and contour plots have been developed to find out the combined effects of input parameters on responses. In addition, the precision of the models was assessed and the output was interpreted and analyzed, which showed high accuracy. Therefore, the desirability function analysis has been applied to LPE's cooling system for multiobjective optimization to maximize the total heat transfer and minimize the cooling system pressure loss simultaneously. Finally, confirmatory tests have been conducted with the optimum parametric conditions to validate the optimization techniques. In conclusion, this methodology optimizes the LPE's cooling system, a 2% increase in the total heat transfer, and a 38% decrease in the pressure loss of the cooling system. These values are considerably large for the LPE design.     

An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine

The present study investigates the possibility of increasing efficiency and lowering pollutant emissions from a syngas-powered engine by modulating operational parameters such as engine load and syngas composition. Although artificial intelligence-based technologies are becoming more common for modeling single-fuel mode engines, they are rarely employed to simulate a dual-fuel syngas/diesel engine. In the first of its kind Endeavor in the area of syngas fueled engines, a hybrid adaptive neuro-fuzzy inference system (ANFIS)-response surface methodology (RSM) is investigated. The performance of syngas (H₂+CO), a novel synthetic gaseous fuel, was studied in four different combinations. ANFIS and RSM-based prediction models were developed using engine performance and emissions data collected over the whole load range. While ANFIS surpassed RSM in model prediction, RSM was useful in establishing mathematical links between engine input and output. The ANFIS model developed had a good correlation between R (0.996–0.9998) and R² (0.992–0.9972), as well as low model errors as determined by the root means square error range (0.0086–5.936) and mean absolute percentage error range (0.0028–0.0194). Theil's U2 was used to calculate the model's uncertainty, which was estimated to be 0.0065–0.0439. The superior forecasting abilities of ANFIS models were proved by low errors and uncertainty. The performance of the syngas-powered engine was optimized using the desirability approach to achieve optimum efficiency while emitting the least amount of pollution. The ideal engine load and syngas compositions for maximum production were 67.99% engine load and 72.4:27.6 as H₂:CO syngas mix.     

Flutter reliability analysis of suspension bridges

A reliability analysis method is proposed in this paper through a combination of the advantages of the response surface method (RSM), finite element method (FEM), first-order reliability method (FORM) and the importance sampling updating method. The method is especially applicable for the reliability evaluation of complex structures of which the limit state surfaces are not known explicitly. After the accuracy and efficiency of the method are demonstrated through numerical examples, the method is used to estimate the flutter reliability of a suspension bridge. The uncertainties such as material properties, geometric parameters, structural damping ratio, flutter derivatives and extreme wind velocity at the bridge site are considered. The example suspension bridge is the Jiang Yin Bridge with a main span length of 1385 m built in China. The results show that the proposed method based on an empirical formula in which the limit state function is explicitly represented as a function of variables overestimates the flutter reliability of suspension bridges. The actual flutter reliability should be more accurately analyzed using the proposed method based on the deterministic finite element method in which the limit state function is implicitly represented as a function of variables. Finally, the most influential random variables on flutter reliability of suspension bridges are identified by using a sensitivity analysis.     

Process modeling and analysis of palm oil mill effluent treatment in an up-flow anaerobic sludge fixed film bioreactor using response surface methodology (RSM)

In this study, the interactive effects of feed flow rate (QF) and up-flow velocity (V up) on the performance of an up-flow anaerobic sludge fixed film (UASFF) reactor treating palm oil mill effluent (POME) were investigated. Long-term performance of the UASFF reactor was first examined with raw POME at a hydraulic loading rate (HRT) of 3 d and an influent COD concentration of 44300 mg/l. Extreme reactor instability was observed after 25 d. Raw POME was then chemically pretreated and used as feed. Anaerobic digestion of pretreated POME was modeled and analyzed with two operating variables, i.e. feed flow rate and up-flow velocity. Experiments were conducted based on a central composite face-centered design (CCFD) and analyzed using response surface methodology (RSM). The region of exploration for digestion of the pretreated POME was taken as the area enclosed by the feed flow rate (1.01, 7.63 l/d) and up-flow velocity (0.2, 3 m/h) boundaries. Twelve dependent parameters were either directly measured or calculated as response. These parameters were total COD (TCOD) removal, soluble COD (SCOD) removal, effluent pH, effluent total volatile fatty acid (TVFA), effluent bicarbonate alkalinity (BA), effluent total suspended solids (TSS), CH4 percentage in biogas, methane yield (Y M), specific methanogenic activity (SMA), food-to-sludge ratio (F/M), sludge height in the UASB portion and solid retention time (SRT). The optimum conditions for POME treatment were found to be 2.45 l/d and 0.75 m/h for QF and V up, respectively (corresponding to HRT of 1.5 d and recycle ratio of 23.4:1). The present study provides valuable information about interrelations of quality and process parameters at different values of the operating variables.