Deep saline aquifers represents one of the most geologically promising solutions for large-scale carbon dioxide (CO2) sequestration, boasting a global storage potential of up to 10,000 Gt of CO2, which far exceeds other subsurface options. This comprehensive review systematically examines recent advancements in aquifers-based CO2 sequestration through experimental studies, simulations, pore-scale analyses, and field applications. Major trapping mechanisms, including structural, residual, solubility, and mineral trapping, are critically evaluated together with key factors affecting storage efficiency, injectivity, and long-term containment security. This study highlights the potential of CO2 foams for enhancing storage efficiency through improved mobility control and sweep efficiency within saline aquifers as overlooked by previous reviews. Additionally, it provides a detailed pore-scale analysis of CO2-brine-rock interactions, providing new insights into multiphase flow dynamics and trapping mechanisms at the microscopic level, which are crucial for accurate reservoir-scale predictions. Moreover, it explore the emerging role of microbial processes in CO2 sequestration, including their impact on mineralization and the security of long-term storage through biogeochemical interactions. In addition, the mechanical integrity of saline aquifers during CO2 injections was discussed in details as not discussed in previous reviews. A dedicated discussion of salt precipitation is presented, including deposition stages, influencing factors such as brine chemistry and injection conditions, and mitigation strategies for maintaining injectivity. The review also identifies key challenges and research gaps for futures researches related to long-term storage prediction, coupled process modeling, and economic feasibility. By integrating insights from pore-scale mechanisms to field-scale applications, this work provides a multidisciplinary framework for advancing aquifers-based CO2 sequestration as a safe and scalable climate mitigation technology.
Microalgae biorefineries offer a route to sustainable fuels, yet most techno-economic analyses (TEAs) and life cycle assessments (LCAs) address narrow product portfolios within cradle-to-gate boundaries. This study presents the first cradle-to-grave TEA and LCA of a commercial-scale, continuous-mode, wastewater-fed photobioreactor (PBR) biorefinery producing 1,000,000 gal/yr biodiesel while treating approximate to 8.9 million m(3)/yr of tertiary wastewater through five-tier cascading valorization of Chlorella vulgaris biomass. Three scenarios were modeled in SuperPro Designer and SimaPro: a biodiesel-plus-fertilizer baseline (S1); added pigment extraction and enzymatic protein hydrolysis (S2); and a full cascade adding two-stage biohythane recovery via dark fermentation and co-digestion (S3). Cascading valorization raised co-product revenue by similar to 600%, cutting the minimum fuel selling price (MFSP) from $198.22 to $158.06/gal (-20.3%); S3 yielded a statistically indistinguishable $159.99/gal, and Monte Carlo simulation confirmed robust separation of S2/S3 from S1. The LCA (ReCiPe Midpoint) showed that PBR cultivation electricity (345 kWh/gal; 90.4% of process-electricity demand) dominated 17 of 18 impact categories, making grid decarbonization the primary improvement lever. Absolute impacts are specific to the fossil-dominated GCC grid, whereas the cultivation-electricity hotspot and the relative effects of cascading valorization generalize across electricity mixes. Valorization deepened land-use credits 3.2-fold via soy-protein displacement but shifted burdens toward water consumption and terrestrial ecotoxicity. Pigment price and electricity cost dominated the sensitivity analysis; under moderate co-product pricing ($7/kg protein, $300/kg pigments), MFSP fell to $16.92-18.73/gal (similar to 89% reduction). Economic allocation per ISO 14044 reduced the biodiesel-attributed GWP to approximate to 13.5-13.8 kg CO2-eq/gal, approaching fossil-diesel parity as co-products absorb most of the burden.
Reinforcement learning has recently emerged as a promising approach for supervisory control and operational optimization in complex industrial systems. In this work, we propose a hierarchical learning-based framework for the energy-efficient real-time operation of a photovoltaic reverse osmosis (PVRO) desalination plant under time-varying freshwater demand. The proposed framework combines a high-level reinforcement learning (RL)-based planning agent, implemented using Proximal Policy Optimization (PPO), with a lowlevel proportional-integral-derivative (PID) controller to jointly address operational efficiency and control stability. Using a dynamic model of the RO process, the PPO agent determines permeate flow-rate set-points by accounting for water demand, storage-tank dynamics, energy consumption, and operational constraints. The selected set-points are tracked by the PID controller, which regulates permeate flow by adjusting the feed pressure of the high-pressure pump, ensuring accurate and stable flow control. The reward formulation penalizes excessive energy use and unsafe storage conditions, including tank overflow and underflow, while promoting demand satisfaction. Simulation results demonstrate effective real-time PVRO operation while maintaining safe storage levels and reliable freshwater production. Under grid-only operation, the PPO-based planner reduces daily grid-energy consumption by approximately 4%-5% compared with rule-based scheduling baselines and achieves near-oracle performance, differing from the day-ahead oracle benchmark by only 17 kWh/day. Under PV-assisted operation, the proposed framework reduces grid-energy consumption from 6754 to 4677 kWh/day, corresponding to a 30.7% reduction relative to grid-only operation. The separation of planning and control layers provides a practical and computationally efficient solution that can be integrated with existing industrial RO control systems.
This paper presents a five-level switched-capacitor multilevel inverter (SCMLI) with dual boosting and common-ground (CG) capability for standalone and grid-connected applications. The proposed converter employs two switched capacitors (SCs), six switches, and two diodes while charging both capacitors only to the input voltage, resulting in reduced capacitor rating and cost. Unlike existing five-level converters, the proposed topology can continue operating as a three-level inverter under the failure of either switched capacitor resulting in improved inverter reliability. To mitigate the large charging-current spikes commonly encountered in SCMLIs, a quasi-soft charging technique using a small charging inductor is introduced, and a practical design methodology is presented. The charging inductor significantly reduces peak capacitor charging current, lowers current stress on semiconductor devices, and improves converter efficiency. The proposed converter is further analyzed in terms of leakage-current mitigation, voltage and current stresses, power loss, efficiency, scalability, and cost, and is quantitatively compared with recently reported common-ground five-level inverters. Experimental results obtained from a 1-kW prototype validate the converter under different operating conditions, including load and modulation variations, while hardware-in-the-loop results demonstrate successful grid-connected operation with effective active and reactive power control.
