This study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.
The paper includes the TG-DTG thermogravimetric air-testing of a cellulose mixture modified with the additives of expanded vermiculite or expanded perlite. A thermal degradation test was carried out at 1000°C with a simultaneous qualitative analysis of the emitted gases. During the thermal degradation process, the thermal effects were also measured. The research results indicate that expanded vermiculite or expanded perlite do not emit toxic gases during thermal degradation. The cellulose mixture modification, with the additives of expanded vermiculite or perlite, does not result in the creation of new gaseous compounds in the process of thermal degradation. A s investigated below, the mixtures tested in this article find application in gating systems for supplying liquid metal in no-bake moulds. Such cellulose-based material solutions shall allow the foundry industry to introduce less gas vaporising technologies within the entire casting production process.