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.

Eucalyptus camdulensis biochar (Ec-bio) was used to adsorb crystal violet (CV) and methylene blue (MB) dyes, which was optimized and further evaluated using different isotherm and kinetic models. Microscopy and spectroscopy techniques showed the interactions of the dyes with the surface functional groups of the Ec-bio, resulting in the removal of the dyes from aqueous solution. Both dyes were immediately uptaken, with equilibrium reached in 60 min, with a higher sorption efficiency of CV compared to MB. Thermodynamic parameters showed endothermic adsorption and the nonspontaneous adsorption of both dyes onto the Ec-bio. Both the adsorption capacity and percentage removal increased with the increasing solution pH from 2.0 to 4.0 and to 10 for CV and MB. An increase in adsorption capacity was observed upon increasing the initial concentrations, with a corresponding decrease in the percentage removal. The pseudo-second-order (PSO) and Elovich kinetic models (nonlinear approach) were a good fit to the data of both dyes, confirming a chemisorptive adsorption process. The Langmuir isotherm fitted well to the CV data, supporting its monolayer adsorption onto the Ec-bio, while the Freundlich isotherm was a good fit to the MB dye data, suggesting the surface heterogeneity of the Ec-bio. The Dubinin–Radushkevich isotherm was a good fit to the adsorption CV data compared with the MB dye, suggesting the physisorption of both dyes onto the Ec-bio due to its mean free energy of adsorption of <8 kJ mol⁻¹.

The production of biosorbents by waste biomass has attracted considerable attention due to the low cost and abundance of the raw materials. Here biochar produced from Eucalyptus camdulensis sawdust (EU-biochar) via pyrolysis at 600 °C was used as a potential biosorbent for Ni²⁺ and Pb²⁺ metal ions from wastewater. Characterization experiments indicated the formation of C- and O-bearing functional groups on the EU-biochar surface, while shifts and changes in the shape of C–H bands suggested the adsorption of Ni²⁺ and Pb²⁺ onto EU-biochar by interacting with surface carboxylic groups. Pb²⁺ was adsorbed more quickly than Ni²⁺, indicating a faster and stronger interaction of Pb²⁺ with EU-biochar compared to Ni²⁺. As the initial concentrations of both metal ions increased, the percentage removal decreased, whereas increasing the EU-biochar dose improved the percentage removal but impaired the adsorption capacity for Ni²⁺ and Pb²⁺. The adsorption capacity could only be improved without affecting the percentage removal of both ions by increasing the pH of the metal solutions. The sorption efficiency of EU-biochar and the removal mechanism of Ni²⁺ and Pb²⁺ were further explored using non-linear and linear forms of kinetic and isotherm models.

Drought forecasting plays a crucial role in drought mitigation actions. Thus, this research deals with linear stochastic models (autoregressive integrated moving average (ARIMA)) as a suitable tool to forecast drought. Several ARIMA models are developed for drought forecasting using the Standardized Precipitation Evapotranspiration Index (SPEI) in a hyper-arid climate. The results reveal that all developed ARIMA models demonstrate the potential ability to forecast drought over different time scales. In these models, the p, d, q, P, D and Q values are quite similar for the same SPEI time scale. This is in correspondence with autoregressive (AR) and moving average (MA) parameter estimate values, which are also similar. Therefore, the ARIMA model (1, 1, 0) (2, 0, 1) could be considered as a general model for the Al Qassim region. Meanwhile, the ARIMA model (1, 0, 3) (0, 0, 0) at 3-SPEI and the ARIMA model (1, 1, 1) (2, 0, 1) at 24-SPEI could be generalized for the Hail region. The ARIMA models at the 24-SPEI time scale is the best forecasting models with high R² (more than 0.9) and lower values of RMSE and MAE, while they are the least forecasting at the 3-SPEI time scale. Accordingly, this study recommends that ARIMA models can be very useful tools for drought forecasting that can help water resource managers and planners to take precautions considering the severity of drought in advance.

Friction head loss equations and friction correction factors were evaluated and compared to field observations collected from thirty center pivots with laterals made of PVCs. The friction head loss equations include Darcy-Weisbach (D-W), Hazen-Williams (H-W), and Scobey, in addition to a proposed equation valid for smooth and rough pipe types and for all turbulent flow types. The proposed equation was developed by combining the equations of D-W and H-W, along with the multiple nonlinear regression technique. The friction correction factors were computed by using the typical Christiansen, modified Christiansen, Anwar, and Alazba formulae. The evaluation has been based on statistical error techniques with observed values as a reference. With the combination of modified Christiansen, Anwar, and Alazba formulae, the results revealed that the magnitudes of friction head loss calculated by using the D-W, H-W, and proposed equations were in agreement with field observations. The root mean square deviation (RMSD) values ranged from 1.6 to 1.7 m. As expected, and when the typical Christiansen friction correction factor was used with the D-W, H-W, and proposed equations, the results showed poor agreement between observed and computed friction head loss values. This was clearly reflected by the high RMSD values that ranged from 5.4 to 5.9 m. On the other hand, agreement occurred between observed friction head loss values and those calculated by using the Scobey equation, invalid for PVC pipe type, when combined with the typical Christiansen formula. This interesting finding led to improved results of the Scobey equation through a developed $C_s$ coefficient suitably valid for PVC pipe type through analytically mathematical derivation; accordingly, the RMSD value dropped from approximately 8.6 to 1.6 m.

This study investigates the application of artificial neural network (ANN) for predicting solar still production (MD). Agricultural drainage water (ADW) was desalinated using a solar still. Important meteorological variables: ambient air temperature, relative humidity, wind speed, and solar radiation, together with the operational variables of flow rate, temperature, and total dissolved solids of feedwater, were considered as input parameters for ANN modeling. The output parameter was MD. The results revealed that the ANN model with five neurons and hyperbolic tangent transfer function was the most appropriate for MD prediction based on the minimum measures of error. The optimal ANN model had a 7–5–1 architecture. The ANN model was also compared to multiple linear regression (MLR). The results indicated that, compared to the MLR model, the ANN model provided better prediction results in all modeling stages. The average of the coefficient of determination between the ANN results and the experimental data was more than 0.96. Consequently, the ANN model was shown to have acceptable generalization capability and accuracy. The relative errors of forecasted MD values for the ANN model were mostly in the vicinity of ±10%. These results indicate that ANN can be successfully used in the MD prediction of a solar still desalinating ADW. One major output/contribution of this research involves assessment of the ANN modeling technique during ADW solar desalination, which adds a new perspective to the system analysis, design, and modeling for the potential productivity of a solar still to produce water during the ADW desalination process.