Modeling and Optimizing of NH4+ Removal from Stormwater by Coal-Based Granular Activated Carbon Using RSM and ANN Coupled with GA

Response surface methodology for process optimization in livestock wastewater treatment: A review

With increasing demand for meat and dairy products, the volume of wastewater generated from the livestock industry has become a significant environmental concern. The treatment of livestock wastewater (LWW) is a challenging process that involves removing nutrients, organic matter, pathogens, and other pollutants from livestock manure and urine. In response to this challenge, researchers have developed and investigated different biological, physical, and chemical treatment technologies that perform better upon optimization. Optimization of LWW handling processes can help improve the efficacy and sustainability of treatment systems as well as minimize environmental impacts and associated costs. Response surface methodology (RSM) as an optimization approach can effectively optimize operational parameters that affect process performance. This review article summarizes the main steps of RSM, recent applications of RSM in LWW treatment, highlights the advantages and limitations of this technique, and provides recommendations for future research and practice, including its cost-effectiveness, accuracy, and ability to improve treatment efficiency.

Composites of Lignin-Based Biochar with BiOCl for Photocatalytic Water Treatment: RSM Studies for Process Optimization

Textile effluents pose a massive threat to the aquatic environment, so, sustainable approaches for environmentally friendly multifunctional remediation methods degradation are still a challenge. In this study, composites consisting of bismuth oxyhalide nanoparticles, specifically bismuth oxychloride (BiOCl) nanoplatelets, and lignin-based biochar were synthesized following a one-step hydrolysis synthesis. The simultaneous photocatalytic and adsorptive remediation efficiency of the Biochar-BiOCl composites were studied for the removal of a benchmark azo anionic dye, methyl orange dye (MO). The influence of various parameters (such as catalyst dosage, initial dye concentration, and pH) on the photo-assisted removal was carried out and optimized using the Box-Behnken Design of RSM. The physicochemical properties of the nanomaterials were characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, thermogravimetric analysis, nitrogen sorption, and UV-Vis diffuse reflectance spectroscopy (DRS). The maximum dye removal was observed at a catalyst dosage of 1.39 g/L, an initial dye concentration of 41.8 mg/L, and a pH of 3.15. The experiment performed under optimized conditions resulted in 100% degradation of the MO after 60 min of light exposure. The incorporation of activated biochar had a positive impact on the photocatalytic performance of the BiOCl photocatalyst for removing the MO due to favorable changes in the surface morphology, optical absorption, and specific surface area and hence the dispersion of the photo-active nanoparticles leading to more photocatalytic active sites. This study is within the frames of the design and development of green-oriented nanomaterials of low cost for advanced (waste)water treatment applications.

Optimization and modeling of ammonia nitrogen removal from anaerobically digested liquid dairy manure using vacuum thermal stripping process

During anaerobic digestion (AD) of liquid dairy manure, organic nitrogen converts to ammonia nitrogen (NH₃-N) and subsequently escalates the NH₃-N concentrations in manure. Among different available NH₃-N removal processes treating anaerobically digested liquid dairy manure (ADLDM), vacuum thermal stripping is reported to be an effective technique. However, none of the studies have performed multi-parameter optimization, which is of utmost significance in maximizing process efficiency. In this study, critical operational parameters for vacuum thermal stripping of NH₃-N from ADLDM were optimized and modeled for the first time via integrating grey relational analysis (GRA)-based Taguchi design, response surface methodology (RSM), and RSM-artificial neural network (ANN). The initial experimental trials conducted using the GRA coupled with Taguchi L₁₆ orthogonal array revealed the order of influence of the process parameters on NH₃-N removal as vacuum pressure (kPa) > temperature (°C) > treatment time (min) > mixing speed (rpm) > pH. The values of the first three most influential operating parameters were then further optimized and modeled using RSM and RSM-ANN models. Under the optimized conditions (temperature: 69.6 °C, vacuum pressure: 43.5 kPa, and treatment time: 87.65 min), the NH₃-N removal efficiency of 93.58 ± 0.59 % was experimentally observed and was in line with the RSM and RSM-ANN models' predicted values. While the RSM-ANN model showed a better prediction potential than did the RSM model when compared statistically. Moreover, the nutrient contents (nitrogen, N and sulfur, S) of the recovered NH₃-N as ammonium sulfate ((NH₄)₂SO₄) were in reasonable agreement with the market-available (NH₄)₂SO₄ fertilizer. The results presented in this study provide important insights into improving the treatment process performance and will help design and operate future pilot- and full-scale vacuum thermal stripping processes in dairy farms.

Optimization of soluble phosphate and IAA production using response surface methodology and ANN approach

Phosphorus (P) is often found inaccessible to plants, as it forms precipitates with cations and can be converted to accessible forms by using Phosphate solubilizing bacteria (PSB). In the present study, isolation and characterization of PSB from rhizospheric soil of coffee plants were performed. The influence of four independent variables (incubation temperature, incubation time, pH, and inoculum size) was investigated and optimized using an artificial neural network and response surface methodology on the solubility of phosphate and indole acetic acid production. The bacterium that can dissolve phosphate were isolated in Pikovskaya's agar containing insoluble tricalcium phosphate. Total, six Phosphate Solubilizing Bacteria were isolated and three of them (PSB1, PSB3, and PSB4) were found to be effectively solubilizing phosphate. Based on phosphate solubilizing index results Pseudomonas bacteria (PSB1) was selected for modeling. The results showed that both models performed reasonably well, but properly trained artificial neural networks have the more powerful modeling capability compared to the response surface method. The optimum conditions were found to be incubation temperature of 37.5 °C, incubation time of 9 days, pH of 7.2, and inoculum size of 1.89 OD. Under these conditions, the model predicted solubility of phosphate of 260.69 μg/ml and production of IAA of 80.00 μg/ml with a desirability value of 0.947. In general, the isolated Pseudomonas is expected to have phosphorus-degrading ability that promotes plant growth, and further field experimental work is required to use this bacterial strain as biofertilizer, as an alternative to synthetic fertilizer.

Comparative Studies of RSM, RSM–GA and ANFILS for Modeling and Optimization of Naphthalene Adsorption on Chitosan–CTAB–Sodium Bentonite Clay Matrix

The aim of this article was to compare the predictive abilities of the optimization techniques of response surface methodology (RSM), the hybrid of RSM–genetic algorithm (RSM–GA) and adaptive neuro-fuzzy interference logic system (ANFILS) for design responses of % removal of naphthalene and adsorption capacity of the synthesized composite nanoparticles of chitosan–cetyltrimethylammonium bromide (CTAB)–sodium bentonite clay.  The process variables considered were surfactant concentration, , activation time, ,  activation temperature, , and chitosan dosage, .  The ANFILS models showed better modeling abilities of the adsorption data on the synthesized composite adsorbent than those of ANN for reason of lower % mean absolute deviation, lower % error value, higher coefficient of determination, , amongst others and lower error functions’ values than those obtained using ANN for both responses.  When applied RSM, the hybrid of RSM–genetic algorithm (RSM–GA) and ANFILS 3–D surface pot optimization technique to determine the optimal conditions for both responses, ANFILS was adjudged the best.  The ANFILS predicted optimal conditions were = 116.00 mg/L, = 2.06 h, = 81.2oC and = 5.20 g.  Excellent agreements were achieved between the predicted responses of 99.055% removal of naphthalene and 248.6375 mg/g adsorption capacity and their corresponding experimental values of 99.020% and 248.86 mg/g with % errors of -0.0353 and 0.0894 respectively.  Hence, in this study, ANFILS has been successfully used to model and optimize the conditions for the treatment of industrial wastewater containing polycyclic aromatic compounds, especially naphthalene and is hereby recommended for such and similar studies.

Shrimp waste-derived porous carbon adsorbent: Performance, mechanism, and application of machine learning

Aquaculture generates significant amount of processing wastes (more than 500 million pounds annually in the United States), the bulk of which ends up in the environment or is used in animal feed. Proper utilization of shrimp waste can increase their economic value and divert them from landfills. In this study, shrimp waste was converted to a porous carbon (named SPC) via direct pyrolysis and activation. SPC was characterized, and its performance for adsorbing ciprofloxacin from simulated water, natural waters, and wastewater was benchmarked against a commercial powdered activated carbon (PAC). The surface area of SPC (2262 m²/g) exceeded that of PAC (984 m²/g) due to abundance of micropores and mesopores. The adsorption of ciprofloxacin by SPC was thermodynamically spontaneous (ΔG = -19 kJ/mol) and fast (k₁ = 1.05/min) at 25 °C. The capacity of SPC for ciprofloxacin (442 mg/g) was higher than that of PAC (181 mg/g). SPC also efficiently and simultaneously removed low concentrations (200 µg/L) of ciprofloxacin, long-chain per- and polyfluoroalkyl substances (PFAS), and Cu ions from water. An artificial neural network function was derived to predict ciprofloxacin adsorption and identify the relative contribution of each input parameter. This study demonstrates a sustainable and commercially viable pathway to reuse shrimp processing wastes.

RSM, ANN-GA and ANN-PSO modeling of SDBS removal from greywater in rural areas via Fe2O3-coated volcanic rocks

Decontamination and reuse of greywater in rural areas has attracted increasing attention. Typical contaminants in grey water are SDBS, which has a stubborn molecular structure. In this study, Fe₂O₃-coated volcanic rocks (Fe₂O₃-VR) prepared from FeCl₃ solution by a heating evaporation method can reach 95% removal of SDBS, which is 80% higher than before. The effect of contact time, pH, initial concentration, FeCl₃ solution concentration, adsorbent dosage and calcination temperature on the removal rate was researched and modeled by response methodology (RSM) and artificial neural network (ANN). Based on the univariate test, the Box-Behnken design method was used to establish the data sample, which represented a quadratic polynomial model with p-value <0.001, R² = 0.9872, while the ANN model has the better performance with R² = 0.9961. The weights of the BP-ANN model were further analyzed using the Garson equation, and the results showed that the validity ranking of the variables was as follows: contact time (37.31%) > calcination temperature (29.43%) > dosage (24.44%) > initial concentration (17.18%) > FeCl₃ solution concentration (17.18%) > pH (11.56%). Genetic algorithm (GA) and particle swarm optimization (PSO) were selected to optimize the process parameters. The results showed that ANN-PSO methodology presented a satisfactory alternative and the predicted removal efficiency was 99.9982% with relative error = 0.2230. The optimum level of contact time, pH, initial SDBS concentration, FeCl₃ solution concentration, adsorbent dosage and calcination temperature is 136.45 min, 5.64, 22.4 mg L⁻¹, 0.3 mol L⁻¹, 83.21 g L⁻¹, 274.02 °C, respectively. Moreover, Fe₂O₃-VR was characterized via instrumental analyses (SEM-EDS, FTIR, XRD, BET).

This paper provides a new method for SDBS removal and parameter optimization of the adsorption process using RSM and ANN models.

Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping

Waste streams with high ammonia nitrogen (NH3-N) concentrations are very commonly produced due to human intervention and often end up in waterbodies with effluent discharge. The removal of NH3-N from wastewater is therefore of utmost importance to alleviate water quality issues including eutrophication and fouling. In the present study, vacuum thermal stripping of NH3-N from high strength synthetic wastewater was conducted using a rotary evaporator and the process was optimized and modeled using response surface methodology (RSM) and RSM–artificial neural network (ANN) approaches. RSM was first employed to evaluate the process performance using three independent variables, namely pH, temperature (°C) and stripping time (min), and the optimal conditions for NH3-N removal (response) were determined. Later, the obtained data from the designed experiments of RSM were used to train the ANN for predicting the responses. NH3-N removal was found to be 97.84 ± 1.86% under the optimal conditions (pH: 9.6, temperature: 65.5 °C, and stripping time: 59.6 min) and was in good agreement with the values predicted by RSM and RSM–ANN models. A statistical comparison between the models revealed the better predictability of RSM–ANN than that of the RSM. To the best of our knowledge, this is the first attempt comparing the RSM and RSM–ANN in vacuum thermal stripping of NH3-N from wastewater. The findings of this study can therefore be useful in designing and carrying out the vacuum thermal stripping process for efficient removal of NH3-N from wastewater under different operating conditions.