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Construction activities have been a primary cause for depleting natural resources and are associated with stern environmental impact. Developing concrete mixture designs that meet project specifications is time-consuming, costly, and requires many trial batches and destructive tests that lead to material wastage. Computational intelligence can offer an eco-friendly alternative with superior accuracy and performance. In this study, coal waste was used as a recycled additive in concrete. The flexural strength of a large number of mixture designs was evaluated to create an experimental database. A hybrid artificial neural network (ANN) coupled with response surface methodology (RSM) was trained and employed to predict the flexural strength of coal waste-treated concrete. In this process, four influential parameters including the cement content, water-to-cement ratio, volume of gravel, and coal waste replacement level were specified as independent input variables. The results show that concrete incorporating 3% recycled coal waste could be a competitive and eco-efficient alternative in construction activities while attaining a superior flexural strength of 6.7 MPa. The RSM-modified ANN achieved superior predictive accuracy with an RMSE of 0.875. Based on the experimental results and model predictions, estimating the flexural strength of concrete incorporating waste coal using the RSM-modified ANN model yielded superior accuracy and can be used in engineering practice to save the effort, cost, and material wastage associated with trial batches and destructive laboratory testing while producing mixtures with enhanced flexural strength. [ABSTRACT FROM AUTHOR]

The petroleum wastewater caused to serious problems in the aqueous and soil environments, therefore treatment of petroleum wastewater has extremely been in the center of attention in recent years. Phenol is one of main petroleum compounds. The photocatalytic removal of phenol from the wastewater by the use of scavenger and nanoparticle is discussed in this research. In this research, Fe/Zn bimetal nanoparticle is synthesized and used by a new and simple method. Based on the results, this nanoparticle has solved the issue of its removal from the water (after the treatment) using its exclusive magnetic property. Using the Hydrogen peroxide as a scavenger has significantly increased the efficiency of the nanoparticle in photocatalytic treatment. The independent parameters studied in this research include the phenol initial concentration, light source power, retention time, pH and the nanocatalysts concentration. The Hydrogen peroxide was used with the constant concentration of 0.01 M. The experiment design was performed by response surface methodology. Based on the results, the removal efficiency was increased by increasing in nanocatalyst concentration, pH, UV source power and retention time. Also removal efficiency decreased by increasing in initial phenol concentration. According to the FESEM result, nanomaterial has dimensions less than 50 nm. The removal efficiency has been obtained as 89% in the optimum condition (pH equal to 5.23, light source power equal to 43.53 w, phenol concentration equal to 50 ppm, Nano concentration equal to 1.57 g/l and the time equal to 58.6 min). [ABSTRACT FROM AUTHOR]