One of the most important types of emerging micropollutants is the pharmaceutical micropollutant. Pharmaceutical micropollutants are usually identified in several environmental compartments, so the removal of pharmaceutical micropollutants is a global concern. This study aimed to remove diclofenac (DCF), ibuprofen (IBP), and naproxen (NPX) from the aqueous solution via cross-linked magnetic chitosan/activated biochar (CMCAB). Two independent factors—pH (4–8) and a concentration of emerging micropollutants (0.5–3 mg/L)—were monitored in this study. Adsorbent dosage (g/L) and adsorption time (h) were fixed at 1.6 and 1.5, respectively, based on the results of preliminary experiments. At a pH of 6.0 and an initial micropollutant (MP) concentration of 2.5 mg/L, 2.41 mg/L (96.4%) of DCF, 2.47 mg/L (98.8%) of IBP, and 2.38 mg/L (95.2%) of NPX were removed. Optimization was done by an artificial neural network (ANN), which proved to be reasonable at optimizing emerging micropollutant elimination by CMCAB as indicated by the high R2 values and reasonable mean square errors (MSE). Adsorption isotherm studies indicated that both Langmuir and Freundlich isotherms were able to explain micropollutant adsorption by CMCAB. Finally, desorption tests proved that cross-linked magnetic chitosan/activated biochar might be employed for at least eight adsorption-desorption cycles.

Using microalgae to remove pharmaceuticals and personal care products (PPCPs) micropollutants (MPs) have attracted considerable interest. However, high concentrations of persistent PPCPs can reduce the performance of microalgae in remediating PPCPs. Three persistent PPCPs, namely, carbamazepine (CBZ), sulfamethazine (SMT) and tramadol (TRA), were treated with a combination of Chaetoceros muelleri and biochar in a photobioreactor during this study. Two reactors were run. The first reactor comprised Chaetoceros muelleri, as the control, and the second reactor comprised Chaetoceros muelleri and biochar. The second reactor showed a better performance in removing PPCPs. Through the response surface methodology, 68.9% (0.330 mg L−1) of CBZ, 64.8% (0.311 mg L−1) of SMT and 69.3% (0.332 mg L−1) of TRA were removed at the initial concentrations of MPs (0.48 mg L−1) and contact time of 8.1 days. An artificial neural network was used in optimising elimination efficiency for each MP. The rational mean squared errors and high R2 values showed that the removal of PPCPs was optimised. Moreover, the effects of PPCPs concentration (0–100 mg L−1) on Chaetoceros muelleri were studied. Low PPCP concentrations (<40 mg L−1) increased the amounts of chlorophyll and proteins in the microalgae. However, cell viability, chlorophyll and protein contents dramatically decreased with increasing PPCPs concentrations (>40 mg L−1).

The ultimate strength of composite columns is a significant factor for engineers and, therefore, finding a trustworthy and quick method to predict it with a good accuracy is very important. In the previous studies, the gene expression programming (GEP), as a new methodology, was trained and tested for a number of concrete-filled steel tube (CFST) samples and a GEP-based equation was proposed to estimate the ultimate bearing capacity of the CFST columns. In this study, however, the equation is considered to be validated for its results, and to ensure it is clearly capable of predicting the ultimate bearing capacity of the columns with high-strength concrete. Therefore, 32 samples with high-strength concrete were considered and they were modelled using the finite element method (FEM). The ultimate bearing capacity was obtained by FEM, and was compared with the results achieved from the GEP equation, and both were compared to the respective experimental results. It was evident from the results that the majority of values obtained from GEP were closer to the real experimental data than those obtained from FEM. This demonstrates the accuracy of the predictive equation obtained from GEP for these types of CFST column.

The potential of microalgal photobioreactors in removing total ammonia nitrogen (TAN), chemical oxygen demand (COD), caffeine (CAF), and N,N-diethyl-m-toluamide (DEET) from synthetic wastewater was studied. Chlorella vulgaris achieved maximum removal of 62.2% TAN, 52.8% COD, 62.7% CAF, and 51.8% DEET. By mixing C. vulgaris with activated sludge, the photobioreactor showed better performance, removing 82.3% TAN, 67.7% COD, 85.7% CAF, and 73.3% DEET. Proteobacteria, Bacteroidetes, and Chloroflexi were identified as the dominant phyla in the activated sludge. The processes were then optimized by the artificial neural network (ANN). High R2 values (>0.99) and low mean squared errors demonstrated that ANN could optimize the reactors’ performance. The toxicity testing showed that high concentrations of contaminants (>10 mg/L) and long contact time (>48 h) reduced the chlorophyll and protein contents in microalgae. Overall, a green technology for wastewater treatment using microalgae and bacteria consortium has demonstrated its high potentials in sustainable management of water resources.

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