Adsorptive of Nickel in Wastewater by Olive Stone Waste: Optimization through Multi-Response Surface Methodology Using Desirability Functions

Pollution from industrial wastewater has the greatest impact on the environment due to the wide variety of wastes and materials that water can contain. These include heavy metals. Some of the technologies that are used to remove heavy metals from industrial effluents are inadequate, because they cannot reduce their concentration of the former to below the discharge limits. Biosorption technology has demonstrated its potential in recent years as an alternative for this type of application. This paper examines the biosorption process for the removal of nickel ions that are present in wastewater using olive stone waste as the biosorbent. Kinetic studies were conducted to investigate the biosorbent dosage, pH of the solution, and stirring speed. These are input variables that are frequently used to determine the efficiency of the adsorption process. This paper describes an effort to identify regression models, in which the biosorption process variables are related to the process output (i.e., the removal efficiency). It uses the Response Surface Method (RSM) and it is based on Box Benken Design experiments (BBD), in which olive stones serves as the biosorbent. Several scenarios of biosorption were proposed and demonstrated by use of the Multi-Response Surface (MRS) and desirability functions. The optimum conditions that were necessary to remove nickel when the dosage of biosorbent was the minimum (0.553 g/L) were determined to be a stirring speed of 199.234 rpm and a pH of 6.369. The maximum removal of nickel under optimized conditions was 61.73%. Therefore, the olive stone waste that was investigated has the potential to provide an inexpensive biosorbent material for use in recovering the water that the nickel has contaminated. The experimental results agree closely with what the regression models have provided. This confirms the use of MRS since this technique and enables satisfactory predictions with use of the least possible amount of experimental data

Determination of water depth in ports using satellite data based on machine learning algorithms

One of the fundamental maintenance tasks of ports is the periodic dredging of them. This is necessary to guarantee a minimum draft that will enable ships to access ports safely. The determination of bathymetries is the instrument that determines the need for dredging and permits an analysis of the behavior of the port bottom over time, in order to achieve adequate water depth. Satellite data processing to predict environmental parameters is used increasingly. Based on satellite data and using different machine learning algorithm techniques, this study has sought to estimate the seabed in ports, taking into account the fact that the port areas are strongly anthropized areas. The algorithms that were used were Support Vector Machine (SVM), Random Forest (RF) and the Multi-Adaptive Regression Splines (MARS). The study was carried out in the ports of Candás and Luarca in the Principality of Asturias. In order to validate the results obtained, data was acquired in situ by using a single beam provided. The results show that this type of methodology can be used to estimate coastal bathymetry. However, when deciding which system was best, priority was given to simplicity and robustness. The results of the SVM and RF algorithms outperform those of the MARS. RF performs better in Candás with a mean absolute error (MAE) of 0.27 cm, whereas SVM performs better in Luarca with a mean absolute error of 0.37 cm. It is suggested that this approach is suitable as a simpler and more cost-effective rough resolution alternative, for estimating the depth of turbid water in ports, than single-beam sonar, which is labor-intensive and polluting