Processes

Mineral extraction from brine solutions is a vital issue for resource recovery in many fields of industry, especially in desalination processes. Usually, the solubility limit is viewed as a key factor that plays a determinant role in the efficiency of a prescribed process. This paper suggests the investigation of the influence of ionic strength, which is a measure of the total concentration of all dissolved ions, on the solubility limits in brines that are extracted from desalination facilities in Kuwait before discharging them into the Persian Gulf. For this purpose, the solubility of two main minerals (CaSO₄ and Mg(OH)₂) was measured for several values of ionic strength achieved by adjusting the concentration of the brine solutions. Brine samples were characterized and concentrated to achieve ionic strength values that are in the range of 1.1–2.0 mol/L. An adapted supersaturation-equilibration method was applied to determine solubility limits. Results show a non-linear relationship between ionic strength and the solubility limit of the target minerals, with behavior similar to that which could be found in the literature. In the case of CaSO₄, it was found that the solubility exhibits an increase (salting in effect) at low ionic strength, followed by a decrease at higher ionic strength (>1.1 M) (salting-out effect). On the other hand, the solubility of Mg(OH)₂ in Kuwait brine water was shown to decrease as the ionic strength increased. These trends, validated against literature data, are attributed to non-ideal solution behavior and specific ion interactions in the complex brine matrix. The findings of this work provide crucial insights for process design, enabling more precise control over precipitation steps and enhancing the overall yield and economic viability of mineral extraction from complex brine resources.

The CO₂ capture process in coal-fired power plant flue gas still faces the difficulties of low material performance and high energy and cost consumption. It is necessary to develop new capture solvents and materials, and also new capture process configurations, to achieve breakthroughs in capture performance and process technology. In various process configurations for CO₂ absorption, lean solution vaporization and compression (LVC) is a commonly used and effective one for reducing the energy and cost consumption. This work propose a partial lean solution vaporization and compression (PLVC) configuration to decrease energy and cost consumption for CO₂ capture, considering the price difference in heat and electricity with the high prices of compressors. The three heat exchange methods of no heat exchange, separate heat exchange, and merged heat exchange for lean solution after flash evaporation are also proposed with PLVC, which could be used in the range of low (0–25%), middle (25–75%), and high split ratios (75–100%) of lean solution for the lowest total heat consumption of the aqueous AMP + PZ solvent. Therefore, the comprehensive cost of the capture process can be minimized by considering different prices of steam heat, electricity, and compression facility.

CO₂ corrosion remains a critical challenge for the safe and reliable operation of Carbon Capture, Utilization, and Storage (CCUS) infrastructure. This review summarizes CO₂ corrosion implications from material selection, exposure time, CO₂ phase behavior, flow conditions, and impurities such as H₂O, O₂, SO_x, NO_x, and H₂S. CO₂ corrosion modeling has, since early works by de Waard in 1975, expanded to a wide range of models and software tools, many of which have already been reviewed and compared. This work provides a historical timeline and a comparative summary of models and software tools to assist in selecting models for CCUS applications. Modeling approaches are classified into empirical, semi-empirical, and mechanistic categories, with their assumptions, strengths, and limitations. CO₂ corrosion modeling has persistent challenges relating to data quality, data quantity, and parameter interactions, which reduce model accuracy, especially for machine learning approaches. The provided perspective emphasizes that machine learning and hybrid modeling approaches for CO₂ corrosion prediction are gaining popularity, and their effectiveness is currently limited by the quality and quantity of available corrosion data. The provided opportunities include recommendations for standardized experimental procedures and hybrid modeling strategies that combine physics-based insights from mechanistic modeling approaches with data-driven machine learning approaches.