Effects of physico-chemical pre-treatment on the performance of an upflow anaerobic sludge blanket (UASB) reactor treating textile wastewater: application of full factorial central composite design

Introducing machine learning model to response surface methodology for biosorption of methylene blue dye using Triticum aestivum biomass

A major environmental problem on a global scale is the contamination of water by dyes, particularly from industrial effluents. Consequently, wastewater treatment from various industrial wastes is crucial to restoring environmental quality. Dye is an important class of organic pollutants that are considered harmful to both people and aquatic habitats. The textile industry has become more interested in agricultural-based adsorbents, particularly in adsorption. The biosorption of Methylene blue (MB) dye from aqueous solutions by the wheat straw (T. aestivum) biomass was evaluated in this study. The biosorption process parameters were optimized using the response surface methodology (RSM) approach with a face-centred central composite design (FCCCD). Using a 10 mg/L concentration MB dye, 1.5 mg of biomass, an initial pH of 6, and a contact time of 60 min at 25 °C, the maximum MB dye removal percentages (96%) were obtained. Artificial neural network (ANN) modelling techniques are also employed to stimulate and validate the process, and their efficacy and ability to predict the reaction (removal efficiency) were assessed. The existence of functional groups, which are important binding sites involved in the process of MB biosorption, was demonstrated using Fourier Transform Infrared Spectroscopy (FTIR) spectra. Moreover, a scan electron microscope (SEM) revealed that fresh, shiny particles had been absorbed on the surface of the T. aestivum following the biosorption procedure. The bio-removal of MB from wastewater effluents has been demonstrated to be possible using T. aestivum biomass as a biosorbent. It is also a promising biosorbent that is economical, environmentally friendly, biodegradable, and cost-effective.

Adsorptive removal of surfactant using dolochar: A kinetic and statistical modeling approach

Disturbingly high rates of consumption of surfactants in household and industries have led to mark them as emerging contaminants in the environment. In the present work, removal of sodium dodecyl sulfate (SDS), an anionic surfactant, using an industrial waste (dolochar) was explored. The adsorbent material was characterized with the help of Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). Kinetic evaluation was performed using first, pseudo-first, second, and pseudo-second order models. Adsorption of SDS over dolochar was expressed best by pseudo-second order kinetic model with regression coefficient (R² ) of .99. Three input parameters including adsorbent dose (20-10 g/L), initial concentration (30-100 mg/L) of the surfactant, and contact time (2-60 min) were chosen for optimization using response surface methodology based on Box-Behnken design (BBD) approach. A total of 15 experiments were run to examine the effect of these variables on removal of SDS by dolochar in a multivariate system. A regression analysis indicated the experimental data fitted well to a quadratic polynomial model with coefficient of regression (R² ) as .99. ANOVA and lack-of-fit test depicted the precision and efficiency of the model. The optimized conditions for SDS removal were found to be adsorbent dose 16.62 g/L, contact time 40 min, and initial concentration 47 mg/L with removal efficiency as 98.91%. PRACTITIONER POINTS: Daily ablutions and use of personal care products introduce a number of surfactants and recalcitrant compounds into the environment. Adsorption is a handy and easy to operate treatment technique to remove graywater pollutants. Kinetic and statistical modeling may be recommended as one of the most prominent tools to understand the removal mechanism. Decentralized treatment of graywater using industrial wastes is recommended as sustainable solution in the developing nations.

Pretreatment technologies for industrial effluents: Critical review on bioenergy production and environmental concerns

The implementation of different pretreatment techniques and technologies prior to effluent discharge is a direct result of the inefficiency of several existing wastewater treatment methods. A majority of the industrial sectors have known to cause severe negative effects on the environment. The five major polluting industries are the paper and pulp mills, coal manufacturing facilities, petrochemical, textile and the pharmaceutical sectors. Pretreatment methods have been widely used in order to lower the toxicity levels of effluents and comply with environmental standards. In this review, the possible environmental benefits and concerns of adopting different pretreatment technologies for renewable energy production and product/resource recovery has been reviewed and discussed.