In the fourth industrial revolution of the water sector, digital technology supports and consolidates the water-human-data nexus. Digital support systems such as computational techniques and artificial intelligence (AI) models have been recently used to collect and analyze data from water and wastewater-related services remotely. As such, workers in wastewater treatment plants (WWTPs) are not exposed to dust, airborne bacteria, and endotoxins for sampling, monitoring, and data collection. Accordingly, the integration of digitalization into environmental studies has found successful applications in the field of wastewater treatment technologies, especially by adsorption.

The performance of the adsorption process is influenced by various environmental factors such as time, temperature, pH, and pollutant concentration. The optimization and automation of these factors are performed appropriately using computational modelling approaches. The commonly used computational AI techniques in wastewater treatment are artificial neural networks (ANNs), fuzzy inference systems (FISs), support vector machines (SVMs), decision tree (DT), and adaptive network-based fuzzy inference system (ANFIS). Adaptive software sensors can also be implemented with AI techniques to conduct appropriate operational controls on the system. An efficient control scheme would result in significant energy and water savings in the adsorption process.

The aim of this Special Issue is to bring together original research and review articles discussing the recent advances in automation, digitalization, and computation in the adsorption process, highlighting the challenges of selecting relevant data, applying hybrid AI tools, and conducting pilot-scale investigations. The Special Issue includes recent approaches to understanding the application of AI methods to predict the performance of different adsorbents for removing metal ions, dyes, pesticides, organic compounds, pharmaceuticals, drugs, and nutrients from the aqueous phase. Moreover, the Special Issue covers the advantages of the computational methods over conventional modelling, including the ability to handle large amounts of noisy data from dynamic and nonlinear systems. We hope that this Special Issue can bring together original research from relevant academic and industry researchers in the field to further meet the Internet of things (IoT) requirements and the fourth industrial revolution.

Potential topics include but are not limited to the following:

• Modelling techniques to describe, understand, predict, or manage the adsorption behaviour for homogeneous solutions and heterogeneous mixtures in wastewater
• Real-world applications of software technologies for wastewater treatment by adsorption
• Model development, evaluation and process identification in adsorption technology for wastewater treatment
• Development and implementation of environmental software, information, and decision support systems in adsorption processes
• Softwares, smart sensors, and remote metering for improving the effectiveness of adsorbents for wastewater treatment
• Aspects and approaches associated with the integrated modelling, assessment, and management of adsorption systems for resource recovery and recycling in wastewater treatment
• Sensitivity and uncertainty assessment for evaluating the pore structure and surface chemistry of adsorbents in wastewater treatment
• Linking the adsorption models to economic estimation for evaluating the system performance
• Implementation of AI techniques and methods (e.g., knowledge-based systems, expert systems, case-based reasoning systems, data mining, multi-agent systems, Bayesian networks, artificial neural networks, fuzzy logic, or knowledge elicitation and knowledge acquisition methods for scaling up the adsorption systems)
• Qualitative, quantitative, mathematical, statistical, and AI models relevant to adsorption phenomena for maintaining intelligent environmental decision support systems
• Computational models, numerical studies, equipment design, and smart systems related to adsorption processes in wastewater treatment