The rapidly increasing population, depleting water resources, and climate change resulting in prolonged droughts and floods have rendered drinking water a competitive resource in many parts of the world. The development of cost-effective and stable materials and methods for providing the fresh water in adequate amounts is the need of the water industry. Traditional water/wastewater treatment technologies remain ineffective for providing adequate safe water due to increasing demand of water coupled with stringent health guidelines and emerging contaminants. Nanotechnology-based multifunctional and highly efficient processes are providing affordable solutions to water/wastewater treatments that do not rely on large infrastructures or centralized systems. The aim of the present study is to review the possible applications of the nanoparticles/fibers for the removal of pollutants from water/wastewater. The paper will briefly overview the availability and practice of different nanomaterials (particles or fibers) for removal of viruses, inorganic solutes, heavy metals, metal ions, complex organic compounds, natural organic matter, nitrate, and other pollutants present in surface water, ground water, and/or industrial water. Finally, recommendations are made based on the current practices of nanotechnology applications in water industry for a stand-alone water purification unit for removing all types of contaminants from wastewater.

Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient R² = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model.

The studies of the kinetics and isotherms adsorption of the Reactive Black 5 (RB5) onto bentonite clay were explored in a batch study in a laboratory. The maximum RB5 adsorption conditions of bentonite clay were optimized such as shaking speed (100 rpm), temperature (323 K), pH (10), contact time (40 min), initial dye concentration (170 mg·L⁻¹), and particle size (177 µm). The adsorbent surface was characterized using Fourier Transform Infrared Spectroscopy spectroscopy. The mechanisms and characteristic parameters of the adsorption process were analyzed using two parameter isotherm models which revealed the following order (based on the coefficient of determination): Harkin-Jura (0.9989) > Freundlich (0.9986) and Halsey (0.9986) > Langmuir (0.9915) > Temkin (0.9818) > Dubinin–Radushkevich (0.9678). This result suggests the heterogeneous nature of bentonite clay. Moreover, the adsorption process was chemisorption in nature because it follows the pseudo-second order reaction model with R² value of 0.9998, 0.9933 and 0.9891 at 25, 75 and 100 mg·L⁻¹ RB5 dye in the solution, respectively. Moreover, based on the values of standard enthalpy, Gibbs free energy change, and entropy, bentonite clay showed dual nature of exothermic and endothermic, spontaneous and non-spontaneous as well as increased and decreased randomness at solid–liquid interface at 303–313 K and 313–323 K temperature, respectively.

Compressive strength is one of the important property of concrete and depends on many factors. Most of the concrete compressive strength predictive models mainly rely on available literature data, which are too simple to consider all the contributing factors. This study adopted a new approach to predict the compressive strength of sugarcane bagasse ash concrete (SCBAC). A vast amount of data from the literature study and fifteen laboratory tested concrete samples with different dosage of bagasse ash, were respectively used to calibrate and validate the models. The novel Gene Expression Programming, Multiple Linear Regression and Multiple Non-Linear Regression were used to model SCBAC compressive strength. The water cement ratio, bagasse ash percent replacement, quantity of fine and coarse aggregate and cement content were used as an input for models development. Various statistical indicators, i.e., NSE, R² and RMSE were used to assess the performance of the models. The results indicated a strong correlation between observed and predicted values with NSE and R² both above 0.8 during calibration and validation for the Gene Expression Programming (GEP). The outcomes from GEP outclassed all the models to predict SCBAC compressive strength. The validity of the model is further verified using data of fifteen tests conducted in the laboratory. Moreover, the cement content in the mix was revealed as the most sensitive parameter followed by water cement ratio form sensitivity analysis. The GEP fulfilled all the criteria for external validity. The simple formulae derived in this study could be used reliably for the prediction of SCBAC compressive strength.

The experimental design of high‐strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python‐based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.