Optimization of operating parameters and microbiological mechanism of a low C/N wastewater treatment system dominated by iron-dependent autotrophic denitrification

Ferrous iron (Fe2+) reduces the amount of external carbon source used for the denitrification of low-C/N wastewater. The effects of key operating parameters on the efficiency of ferrous-dependent autotrophic denitrification (FDAD) and the functioning mechanism of the microbiome can provide a regulatory strategy for improving the denitrification efficiency of low C/N wastewater. In this study, the response surface method (RSM) was used to explore the influence of four important parameters-the molar ratio of Fe2+ to NO3--N (Fe/N), total organic carbon (TOC), the molar ratio of inorganic carbon to NO3--N (IC/N) and sludge volume (SV, %)-on the FDAD efficiency. Functional prediction and molecular ecological networks based on high-throughputs sequencing techniques were used to explore changes in the structure, function, and biomarkers of the sludge microbial community. The results showed that Fe/N and TOC were the main parameters affecting FDAD efficiency. Higher concentrations of TOC and high Fe/N ratios provided more electron donors and improved denitrification efficiency, but weakened the importance of biomarkers (Rhodanobacter, Thermomonas, Comamonas, Thauera, Geothrix and unclassified genus of family Gallionellaceae) in the sludge ecological network. When Fe/N > 4, the denitrification efficiency fluctuated significantly. Functional prediction results indicated that genes that dominated N2O and NO reduction and the genes that dominated Fe2+ transport showed a slight decrease in abundance at high Fe/N levels. In light of these findings, we recommend the following optimization ranges of parameters: Fe/N (3.5-4); TOC/N (0.36-0.42); IC/N (3.5-4); and SV (approximately 35%).

Development of response surface method prediction model for traffic-related roadside noise levels based on traffic characteristics

Recently, several urban areas are trying to mitigate the environmental impacts of traffic, where noise pollution is one of the main consequences. Thus, studying the determinants of traffic-related noise generation and developing a model that predicts the level of noise by controlling the influencing factors are crucial for transportation planning purposes. This research aims at utilizing the response surface method (RSM) to develop a robust statistical prediction model of traffic-related noise levels and optimize different traffic characteristics' ranges to reduce the expected noise levels. The results indicate that the rate of Leq increase is higher at traffic flow values less than the 1204 veh/h. The interaction effect of flow-speed and flow-heavy vehicle percentage pairs shows that Leq has peak values around 45.8 km/h and 28.71%, respectively, with almost symmetric value distribution about those center points. The main effects study indicates a direct effect of traffic flow, speed, density, and traffic composition on roadside noise levels. The prediction model has good representativeness of observed noise levels by predicted noise levels as the model has a high coefficient of determination (R2 = 95.87% and R2 adj = 92.26%) with a significance level of 0.0036. Then, the research presents a methodology to perform an optimization of the roadside noise level by defining traffic characteristics that can keep the noise level below 65 dB(A) or minimize noise level. Decision-makers could use the proposed method to control the roadside noise level.

Bifunctional catalytic degradation of diclofenac over Cu-Pd co-modified sponge iron-based trimetal: Parameter optimization

Currently, the pharmaceutical and personal care products (PPCPs) have posed great challenge to advanced oxidation techniques (AOTs). In this study, we decorated sponge iron (s-Fe⁰) with Cu and Pd (s-Fe⁰-Cu-Pd) and further optimized the synthesis parameters with a response surface method (RSM) to rapidly degrade diclofenac sodium (DCF). Under the RSM-optimized conditions of Fe: Cu: Pd = 100: 4.23: 0.10, initial solution pH of 5.13, and input dosage of 38.8 g/L, 99% removal of DCF could be obtained after 60 min of reaction. Moreover, the morphological structure of trimetal was characterized with high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), X-ray diffraction (XRD), X-ray photoelectron spectra (XPS). Electron spin resonance (ESR) signals have also been applied to capture reactive hydrogen atoms (H*), superoxygen anions, hydroxyl radicals, and single state oxygen (¹O₂). Furthermore, the variations of DCF and its selective degradation products over a series of s-Fe⁰-based bi(tri)metals have been compared. Additionally, the degradation mechanism of DCF has also been explored. To our best knowledge, this is the first report revealing the selective dechlorination of DCF with low toxicity over Pd-Cu co-doped s-Fe⁰ trimetal.

Modeling and optimization of the coagulation/flocculation process in turbidity removal from water using poly aluminum chloride and rice starch as a natural coagulant aid

The application of the coagulation/flocculation process is very important due to its simplicity in removing turbidity. Due to the disadvantages of using chemical coagulants in water and the lack of sufficient effect of natural materials alone in removing turbidity for proper performance, the simultaneous use of chemical and natural coagulants is the best way to reduce the harmful effects of chemical coagulants in water. In this study, the application of poly aluminum chloride (PAC) as a chemical coagulant and rice starch as a natural coagulant aid to remove turbidity from aqueous solutions was investigated. Effects of the above coagulants on the four main factors, coagulant dose (0-10 mg/L), coagulant adjuvant dose (0-0.1 mg/L), pH (5-9), turbidity (NTU 0-50), and each five levels were assessed using a central composite design (CCD). Under the optimized conditions, the maximum turbidity elimination efficiency was found to be 96.6%. The validity and adequacy of the proposed model (quadratic model) were confirmed by the corresponding statistics (i.e., F-value of 23.3, p-values of 0.0001, and lack of fit of 0.877 for the model, respectively, R2 = 0.88, R2adj. = 0.84, R2 pred = 0.79, AP = 22.04).

Machine learning and statistical analysis for biomass torrefaction: A review

Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kinetic analysis, process modeling, and computational fluid dynamics, have been used to explain torrefaction performance and characteristics. However, they may be insufficient in actual applications because of providing only some specific solutions. Machine learning (ML) and statistical approaches are powerful tools for analyzing and predicting torrefaction outcomes and even optimizing the thermal process for its utilization. This state-of-the-art review aims to present ML-assisted torrefaction. Artificial neural networks, multivariate adaptive regression splines, decision tree, support vector machine, and other methods in the literature are discussed. Statistical approaches (SAs) for torrefaction, including Taguchi, response surface methodology, and analysis of variance, are also reviewed. Overall, this review has provided valuable insights into torrefaction optimization, which is conducive to biomass upgrading for achieving net zero.

Optimization of Cordyceps sinensis fermentation Marsdenia tenacissima process and the differences of metabolites before and after fermentation

In this paper, we explored the interaction of factors which influenced the Cordyceps sinensis fermentation Marsdenia tenacissima (Roxb.) Wight et Arn, a Dai (a national minority of China) medicine, and the optimal fermentation conditions. The differences of C. sinensis metabolites in normal state (CN) and products of two-way liquid fermentation of C. sinensis and Marsdenia tenacissima (CM) and Marsdenia tenacissima (MT). The interactive effect of factors was analyzed and the best conditions are obtained through the box-behnken design (BBD) in response surface methodology (RSM). All metabolites were determined by ultra high performance liquid chromatography quadrupole time of flight mass spectrometer (UHPLC-Q-TOF-MS), analyzed and identified by metabonomics technology. Results showed that the optimum fermentation conditions were the concentration of raw medicinal materials is 160 g/L, the fermentation time is 6 days, the inoculation volume is 9.5%, the rotating speed is 170 rpm. 197 metabolites were identified in both positive ion and negative ion. 119 metabolites were significantly different between CN and CM. 43 metabolites were significantly different between CM and MT. Differential metabolic pathways were enriched. In conclusion, this paper optimizes the bidirectional fermentation process of M. tenacissima and C. sinensis through response surface methodology, and analyzes the changes of components from the level of metabonomics, so as to provide reference for exploring medicinal fungi fermentation of traditional Chinese medicine.

Improved glucose yield and concentration of sugarcane bagasse by the pretreatment with ternary deep eutectic solvents and recovery of the pretreated liquid

In this study, a novel ternary deep eutectic solvents (DES) consisting of choline chloride/PEG/hydroxyethyl sulfonic acid (HSA) was developed to effectively improve glucose yield and concentration of sugarcane bagasse, and the conditions of the pretreatment were optimized by response surface method (RSM). Under the optimal conditions, the maximum glucose concentration (GC) could reach 12.39 g/L (HSA concentration 1.34 %, PEG400, 2.3 h, 150 °C), and the maximum glucose yield (GY) was 0.2497 g/g (HSA concentration 1.41 %, PEG400, 2.1 h, 150 °C). Hemicellulose was completely removed, and the maximum lignin removal rate was 86.89 %. After pretreatment, 95 % of the pretreated liquid can be recycled. Finally, the structural and morphological changes of bagasse before and after pretreatment were investigated by scanning electron microscopy (SEM), Fourier Transform infrared analyzer (FT-IR) and X-ray diffraction (XRD).

Predicting hemodynamic indices in coronary artery aneurysms using response surface method: An application in Kawasaki disease

BACKGROUND AND OBJECTIVES

Coronary artery aneurysms (CAA), such as those in Kawasaki Disease (KD), induce hemodynamic alterations associated with thrombosis and atherosclerosis. Current clinical routines assess the risk level of the CAA cases based on the Z-Score, which considers the body surface area (BSA) and the CAA's diameter. A full geometric characterization and impact on hemodynamic metrics and their correlation with thrombotic risks have not been systematically investigated. The goal of this study was to investigate the effect of CAA shape indices on local hemodynamics using the response surface method (RSM) through considering KD applications.

METHODS

Transient computational fluid dynamics (CFD) simulations have been performed on idealized CAA geometries defined by geometrical ratios combining neck diameter, CAA diameter and CAA length. The results were used to develop full quadratic regression models of the indices using the response surface method (RSM). Validation using patient-specific KD models was performed.

RESULTS

The results indicated that the aneurysm diameter is the main determining factor in the thrombotic risk of CAA patients, which is consistent with clinical guidelines. Furthermore, it was observed that in most CAA cases having the same diameter, the one with the shorter length experiences higher RRT values, indicating flow stagnation and circulation.

CONCLUSIONS

The developed regression models can be used to ultimately assess the thrombotic risk of CAA cases from the hemodynamic perspective. The applicability of these models was tested on 2 KD patient specific models, with close values achieved between the models and the patient-specific results.

Experimental analysis and parameter optimization on the reduction of NOx from diesel engine using RSM and ANN Model

The major emission sources of NO_X are from automobiles, trucks, and various non-road vehicles, power plants, coal fired boilers, cement kilns, turbines, etc. Plasma reactor technology is widely used in gas conversion applications, such as NOx conversion into useful chemical by-product. Among the plasma treatment techniques, nonthermal plasma (NTP) is widely used because it does not cause any damage to the surfaces of the reacting chamber. In this proposed work, the feasibility of Dielectric Barrier Discharge (DBD) reactor-based nonthermal plasma (NTP) process is examined based on four operating parameters including NOx concentration (300-400 ppm), gas flow rate (2-6 lpm), applied plasma voltage (20-30 kVpp), and electrode gap (3-5 mm) for removing NOx gas from diesel engine exhaust. Optimization of NTP process parameters has been carried out using response surface-based Box-Behnken design (BBD) method and artificial neural network (ANN) method and compared with the performance measures such as R², MSE (mean square error), RMSE (root mean square error), and MAPE (mean absolute percentage error). Two kinds of analysis were carried out based on (1) NOx removal efficiency and (2) energy efficiency. Based on the simulation studies carried out for Nox removal efficiency, the RSM methodology produces the performance measures, 0.98 for R², 1.274 for MSE, 1.128 for RMSE, and 2.053 for MAPE, and for ANN analysis method, 0.99 for R², 2.167 for MSE, 1.472 for RMSE, and 1.276 for MAPE. These results shows that ANN method is having enhanced performance measures. For the second case, based on the energy efficiency study, the R², MSE, RMSE, and MAPE values from the RSM model are 0.97, 2.230, 1.493, and 2.903 respectively. Similarly based on ANN model, the R², MSE, RMSE, and MAPE values are 0.99, 0.246, 0.46, and 0.615, respectively. From the performance measures, it is found that the ANN model is accurate than the RSM model in predicting the NOx removal/reduction and efficiency. These models demonstrate that they have strong agreement with the experimental results. The experimental results are indicated that optimum conditions arrived based on the RSM model resulted in a maximum NOx reduction of 60.5% and an energy efficiency of 66.24 g/J. The comparison between the two models confirmed the findings, whereas this ANN model displayed a stronger correlation to the experimental evidence.

Optimization of Fenton process for removing TOC and color from swine wastewater using response surface method (RSM)

The Fenton oxidation process was applied to biologically treated swine wastewater (BSWW) for the removal of TOC and color constituents after coagulation with FeCl₃. Optimizing of operational variables such as FeSO₄ and H₂O₂ doses was achieved by the response surface method (RSM). Statistical analysis led to the conclusion that FeSO₄ is the more important than H₂O₂ in the removal of TOC. However, H₂O₂ plays a more significant role than FeSO₄ in color removal. The optimal conditions for effective removal of TOC and color from swine wastewater were derived by using process optimization. The experimental results show that overall removal of TOC and color is 76.7% and 98%, respectively, when optimal conditions of 800 mg/L (FeSO₄) and 5207 mg/L (H₂O₂) at 120 min were used. Furthermore, the optimization model produces a desirability value of 0.980 that verifies the optimal conditions. Finally, it is observed that removal of undesirable compounds follows a pseudo-first order and pseudo-second order kinetics model with high R² values of 0.99 for both TOC and color removal, respectively. Statistical analysis and process optimization show that the employed model may determine conditions conducive to the effective removal of TOC and color from swine wastewater.

Response surface method for modeling the removal of carbon dioxide from a simulated gas using water absorption enhanced with a liquid-film-forming device

This paper presents the results from using a physical absorption process to absorb gaseous CO₂ mixed with N₂ using water by producing tiny bubbles via a liquid-film-forming device (LFFD) that improves the solubility of CO₂ in water. The influence of various parameters-pressure, initial CO₂ concentration, gas-to-liquid ratios, and temperature-on the CO₂ removal efficiency and its absorption rate in water were investigated and estimated thoroughly by statistical polynomial models obtained by the utilization of the response surface method (RSM) with a central composite design (CCD). Based on the analysis, a high efficiency of CO₂ capture can be reached in conditions such as low pressure, high CO₂ concentration at the inlet, low gas/liquid ratio, and low temperature. For instance, the highest removal efficiency in the RSM-CCD experimental matrix of nearly 80% occurred for run number 20, which was conducted at 0.30MPa, CO₂ concentration of 35%, gas/liquid ratio of 0.71, and temperature of 15°C. Furthermore, the coefficients of determination, R², were 0.996 for the removal rate and 0.982 for the absorption rate, implying that the predicted values computed by the constructed models correlate strongly and fit well with the experimental values. The results obtained provide essential information for implementing this method properly and effectively and contribute a promising approach to the problem of CO₂ capture in air pollution treatment.

Optimization of monomethoxy polyethyleneglycol-modified oxalate decarboxylase by response surface methodology

In order to improve the stability of oxalate decarboxylase (Oxdc), response surface methodology (RSM), based on a four-factor three-level Box-Behnken central composite design was used to optimize the reaction conditions of oxalate decarboxylase (Oxdc) modified with monomethoxy polyethyleneglycol (mPEG5000). Four independent variables such as the ratio of mPEG-aldehyde to Oxdc, reaction time, temperature, and reaction pH were investigated in this work. The structure of modified Oxdc was identified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Fourier transform infrared (FTIR) spectroscopy, the stability of the modified Oxdc was also investigated. The optimal conditions were as follows: the mole ratio of mPEG-aldehyde to Oxdc of 1:47.6, time of 13.1 h, temperature at 29.9 °C, and the reaction pH of 5.3. Under optimal conditions, experimental modified rate (MR = 73.69%) and recovery rate (RR = 67.58%) were matched well with the predicted value (MR = 75.11%) and (RR = 69.17%). SDS-PAGE and FTIR analysis showed that mPEG was covalently bound to the Oxdc. Compared with native Oxdc, the modified Oxdc (mPEG-Oxdc) showed higher thermal stability and better tolerance to trypsin or different pH treatment. This work will provide a further theoretical reference for enzyme modification and conditional optimization.

Optimizing culture conditions for production of intra and extracellular inulinase and invertase from Aspergillus niger ATCC 20611 by response surface methodology (RSM)

The aim of this study was obtain a model that maximizes growth and production of inulinase and invertase by Aspergillus niger ATCC 20611, employing response surface methodology (RSM). The RSM with a five-variable and three-level central composite design (CCD) was employed to optimize the medium composition. Results showed that the experimental data could be appropriately fitted into a second-order polynomial model with a coefficient of determination (R²) more than 0.90 for all responses. This model adequately explained the data variation and represented the actual relationships between the parameters and responses. The pH and temperature value of the cultivation medium were the most significant variables and the effects of inoculum size and agitation speed were slightly lower. The intra-extracellular inulinase, invertase production and biomass content increased 10-32 fold in the optimized medium condition (pH 6.5, temperature 30°C, 6% (v/v), inoculum size and 150rpm agitation speed) by RSM compared with medium optimized through the one-factor-at-a-time method. The process development and intensification for simultaneous production of intra-extracellular inulinase (exo and endo inulinase) and invertase from A. niger could be used for industrial applications.