Statements of fact and opinion in the articles in the Journal of Applied Materials and Technology are those of the respective authors and contributors and not of Journal of Applied Materials and Technology or the institution of Applied Materials and Technology Society and Faculty of Engineering, Universitas Riau. Neither Applied Materials and Technology Society and Faculty of Engineering, Universitas Riau nor Journal of Applied Materials and Technology make any representation, express or implied, in respect of the accuracy of the material in this journal and cannot accept any legal responsibility or liability for any errors or omissions that may be made by the reader should make her or his own evaluation as to the appropriateness or otherwise of any experimental technique described.

Claus process is a widely adopted process to reduce emissions from refineries by converting H₂S into elemental sulfur. On the other hand, Haldor Topsoe’s Wet Gas Sulfuric Acid (WSA) is an alternative to convert H₂S directly into sulfuric acid. The purpose of this project was to simulate both of these state-of-the-art technologies and evaluate their suitability for various acid gas capacity and H₂S concentrations. Three sustainability pillars of people (safety), planet (environment), and profit were used as the comparison metrics. The developed simulation (1^st principle) models were used to generate lots of data as the basis for subsequent development of regression models. The latter models were used in the comparisons for they are much faster in calculations than the 1^st principle models. The results showed that the WSA process was safer (lower Fire and Explosion Damage Index), more environmentally friendly (lower Global Warming Potential), and more profitable (higher annual profit) in most of the evaluated operating conditions.

Wastes generated in large amounts have been recognized as sustainable sources of raw materials for the synthesis of adsorbents. The synthesis of zeolite through wastes recycling of two different ash sources (coal bottom ash and sugarcane waste ash) and industrial aluminum waste was evaluated. The molar ratio of SiO₂/Al₂O₃ for zeolite 4A formation was achieved by the addition of aluminum waste from tertiary industry as aluminum source. Coal bottom ash and sugarcane waste ash were used as a source of both silica and alumina. The synthesized materials were characterized using X-ray powder diffraction (XRD), scanning electron microscopy (SEM) and cation exchange capacity (CEC). The analysis of the properties of the products demonstrates that the by-products can be used to produce zeolite A. The utilization of synthesized zeolites as adsorbent for cadmium removal from aqueous solution was conducted following the concept of implementation of utilization of waste materials as a component of the circular economy in the wastewater sector.

The recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. This development leads to assist people with disabilities to benefit from neuroprosthetic devices that improve the life of those suffering from neurological disorders. This paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. Therefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. The paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).

Motor imagery based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalogram (EEG) signals to study brain activity with left and right-hand movement. Deep learning (DL) has been employed for motor imagery (MI). In this article, a deep neural network (DNN) is proposed for classification of left and right movement of EEG signal using Common Spatial Pattern (CSP) as feature extraction with standard gradient descent (GD) with momentum and adaptive learning rate LR. (GDMLR), the performance is compared using a confusion matrix, the average classification accuracy is   87%, which is improved as compared with state-of-the-art methods that used different datasets.

Metal organic frameworks (MOFs) are frequently used as adsorbents in adsorption processes to remove dyes from effluent produced by the textile industry. Today, dye contaminants have become an important environmental problem. One of these dyes is methylene blue (MB) and its removal from wastewater is a priority because it is persistent and nondegradable. MB is used in many industries although it has potential harmful effects on human and aquatic life and can be considered a hazardous chemical when in wastewater. The present study shows the potential applications for enhanced forms of UiO-66 MOFs, such as UiO-66, UiO-66-10%Al and UiO-66-30%Al. These forms were prepared to remove MB from wastewater using batch experiments. Characterisation of adsorbents were accomplished successfully using Fourier transform infrared, X-ray powder diffraction, Brunauer–Emmett–Teller surface area and thermogravimetric analysis techniques. To investigate equilibrium adsorptive behaviour, Langmuir and Freundlich isotherm models were tested against the experimental data. Based on linear regression correlation coefficient (R²), the Freundlich model described the equilibrium isotherm of MOF/MB better than the Langmuir model. Of all forms of UiO-66 MOF, UiO-66-10%Al had the maximum Langmuir adsorption capacity at 49.26 mg/g. A kinetics study examined pseudo first-order, pseudo second order and Elovich models to determine which could explain the sorption mechanism. While the pseudo second order and Elovich models showed a good fit with the experimental data, the correlation coefficient of the pseudo second-order model was the highest. These results indicate that adsorption of MB is controlled by a chemisorption mechanism. Further, intraparticle diffusion was utilised to describe the adsorption mechanism and determine the rate-limiting steps in the adsorption process.