Innovating methods for treating industrial wastewater containing heavy metals frequently incorporate toxicity-reduction technologies to keep up with regulatory requirements. This article reviews the latest advances, benefits, opportunities and drawbacks of several heavy metal removal treatment systems for industrial wastewater in detail. The conventional physicochemical techniques used in heavy metal removal processes with their advantages and limitations are evaluated. A particular focus is given to innovative gas hydrate-based separation of heavy metals from industrial effluent with their comparison, advantages and limitations in the direction of commercialization as well as prospective remedies. Clathrate hydrate-based removal is a potential technology for the treatment of metal-contaminated wastewater. In this work, a complete assessment of the literature is addressed based on removal efficiency, enrichment factor and water recovery, utilizing the gas hydrate approach. It is shown that gas hydrate-based treatment technology may be the way of the future for water management purposes, as the industrial treated water may be utilized for process industries, watering, irrigation and be safe to drink.

This paper presents an experimental and modeling studies on the thermodynamic inhibition effects of the mixture of monoethlyene glycol (MEG) and glycine (Gly) on the carbon dioxide hydrate phase boundary condition. The monoethlyene glycol and glycine (1:1) mixture inhibition effects were investigated at concentrations of 5, 10, and 15 wt.% and pressure ranges from 2.0–4.0 MPa. The effects of the proposed mixture on the carbon dioxide hydrate phase boundary were evaluated by measuring the dissociation temperature of carbon dioxide hydrate using a T-cycle method. The synergistic effect was evaluated based on comparison with pure MEG and Gly data. The results show that 15 wt.% of MEG and Gly mixture displays the highest inhibition effect compared to the 5 and 10 wt.% mixtures, respectively. However, the synergistic effect is higher at 10 wt.%. Dickens' model was also adopted to predict the phase equilibrium data of CO₂ hydrates in the presence of the mixture. The modified model successfully predicted the data within a maximum error of ± 0.52 K.

In this study, series of non-ionic surfactants from Span and Tween are evaluated for their ability to affect the viscosity profile of cyclopentane hydrate slurry. The surfactants; Span 20, Span 40, Span 80, Tween 20, Tween 40 and Tween 80 were selected and tested to provide different hydrophilic–hydrophobic balance values and allow evaluation their solubility impact on hydrate formation and growth time. The study was performed by using a HAAKE ViscotesterTM 500 at 2 °C and a surfactant concentration ranging from 0.1 wt%–1 wt%. The solubility characteristic of the non-ionic surfactants changed the hydrate slurry in different ways with surfactants type and varying concentration. The rheological measurement suggested that oil-soluble Span surfactants was generally inhibitive to hydrate formation by extending the hydrate induction time. However, an opposite effect was observed for the Tween surfactants. On the other hand, both Span and Tween demonstrated promoting effect to accelerate hydrate growth time of cyclopentane hydrate formation. The average hydrate crystallization growth time of the blank sample was reduced by 86% and 68% by Tween and Span surfactants at 1 wt%, respectively. The findings in this study are useful to understand the rheological behavior of surfactants in hydrate slurry.

The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO₂ capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.