Corrigendum to "A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique" [Heliyon 9(4) (April 2023) e15455].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e15455.].

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique.

Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.

The effect of waste medical radiology as fiber reinforcement on the behavior of eco-efficient self-compacting concrete.

An effort is being conducted to enhance some characteristics of self-compacted concrete (SCC) and clean the environment through the addition of waste plastic fibers resulting from the cuts of waste medical radiology. A number of tests were carried out to examine the impact of waste medical radiology (WMR) fiber additions with various aspect ratios and various percentages on SCC characteristics. Thus, various SCC mixes were designed at a constant water-to-binder ratio of 0.33 and 550 kg/m3 of binder content. The four groups of WMR fiber content were specified with different aspect ratios of (0, 40, 50, and 60) with various ratios of (1%, 1.25, and 1.5%) by volume of concrete. The workability characteristics of SCC mixes were determined by fresh density, segregation resistance, L-box height ratio, T50 slump with V-funnel flow time, and slump flow diameter. Also, the measurement of thermal conductivity, compressive, flexural, and splitting tensile strengths were performed at 28 days for SCC mixtures. The findings revealed that WMR fibers have a negative impact on the fresh characteristics of SCC except for segregation resistance, which improved. However, the results of splitting tensile and compressive strengths were enhanced at 1% WMR fiber content with various aspect ratios then decreased. However, all results of flexural strength were reduced in comparison with the control mixture excluding samples containing 1% WMR fibers with an aspect ratio of 50 which showed a higher result. The outcomes of thermal conductivity were reduced with the usage of various WMR fiber percentages and various aspect ratios in comparison with the control mixture, and the best result was obtained at 1.25% WMR fiber with an aspect ratio of 50.