This work describes a pilot-scale, thermally controlled gas–liquid absorption column designed for point-source carbon dioxide (CO₂) capture, featuring a 4-m tall, 0.3-m diameter absorber with a capacity of up to 1 t of CO₂ per day. Flue-gas conditions are simulated using a natural gas-fired power generation engine. A key advancement is the integration of a 3D-printed proce…
Hollow fiber membrane modules are used for gas purification by their selective permeation properties. Intensification of the process to minimize the retentate loss and gas pressure involves optimization at various scales. In this work, we outline a numerical investigation of the gas separation performance at the scale of fiber bundles and its impact on module performance.…
Scientific discovery increasingly depends on computing and software. Yet the practices used to develop scientific software often differ markedly from those in professional software engineering. To help bridge this gap, IEEE Computing in Science & Engineering (CiSE) is merging its existing Software Engineering and Scientific Programming departments into a unified department…
Streamflow prediction is essential for water resources management, flood forecasting, and climate resilience. Long short-term memory (LSTM) networks have advanced large-sample hydrology through cross-basin learning, but their recurrent architectures have limited ability to capture long-range temporal dependencies, particularly for medium-range forecasting. Meanwhile, exist…
Electrical resistivity tomography (ERT) is a subsurface imaging geophysical technique. Traditional ERT inversion methods, such as smoothness-constrained least-squares approaches, often suffer from discretization artifacts when reconstructing electrical resistivity from resistance measurements. To address the limitations of conventional ERT inversion, this study introduces…
Data-driven rainfall–runoff models have advanced rapidly, yet the majority of large-scale applications still rely on lumped inputs that smooth out spatial variability in precipitation, temperature, and landscape properties. This simplification can introduce substantial biases in flood peaks, hydrograph timing, and water balance estimates. Here, we develop a deep learning f…