Processes

Gas analyzers in post-combustion CO₂ capture plants are accurate but slow and sequential, yielding sparse, non-synchronous concentration records across absorber stages. We address this missing-data problem by reconstructing continuous CO₂ profiles with Moving Horizon Estimation (MHE) constrained by a mechanistic absorber model and available measurements; these MHE reconstructions are used as supervisory labels to train an end-to-end Stacked Denoising Autoencoder–Gated Recurrent Unit (SDAE-GRU) model. At run time, we deploy a hybrid soft sensor using the SDAE-GRU together with the mechanistic model and fuse their open-loop predictions via covariance-weighted blending with Gaspari-Cohn localization. We validate this approach on a pilot-scale MEA absorber using data from seven pilot runs conducted at distinct operating conditions, using datasets 1–5 for training/tuning and 6–7 for blind validation. On the blind validation runs, the hybrid estimator achieves a MAPE of 3.79% for stage-wise CO₂ predictions (averaged over all stages and time samples), outperforming both constituents evaluated standalone: 7.86% for the GRU-only soft sensor and 6.79% for the mechanistic model. Because MHE is used only offline to generate labels and to estimate model-error covariances, the deployed estimator is lightweight and suitable for online monitoring.

Asbestos-containing materials (ACMs) are among the most common types of hazardous building materials. Usually, ACMs are identified by laboratory methods, which can slow down and complicate the processes of demolition and refurbishment of old buildings. The hypothesis applied in this study is that ACMs, both in friable and non-friable forms, can be reliably identified in situ using a handheld Raman spectrometer (HHRS). A HHRS equipped with two temperature-controlled diode lasers (785 nm and 852 nm) was used. Two groups of ACMs were examined: one, consisting of ACMs with a known type of asbestos, previously determined by standardised tests used for the HHRS method’s validation, and the second, consisting of presumed ACMs, where HHRS was used for the identification of asbestos. Additional testing according to ISO 22262-1 was applied. The impact of several factors on the asbestos identification was evaluated. The results confirm that the identification by HHRS of all main types of asbestos minerals is possible with a certain level of probability, regardless of whether the fibres are in an unbound form (fabrics, ropes, wools) or bound within cementitious or polymer composites. Some processing (scaling, smoothing) of the reference spectra should be applied to increase the percentage of asbestos minerals’ identification. In conclusion, it has been proven that the majority of ACM in buildings may be identified in situ by HHRS in a rapid manner, thus accelerating the pre-demolition/pre-renovation audit (PDA/PRA), avoiding risks to demolition/refurbishment workers’ health due to asbestos unawareness, as well as preventing the contamination of other CDW and environmental pollution.

Cold Heavy Oil Production with Sands (CHOPS) creates high-permeability wormhole networks that strongly influence post-CHOPS recovery performance. Although CSI is a promising post-CHOPS recovery method, the coupled effects of wormhole coverage and pressure depletion strategy on oil recovery remain insufficiently understood. In this study, microfluidic systems were employed to investigate the combined influence of wormhole length and pressure depletion strategy on CSI performance. Micromodels with varying wormhole lengths were used under different pressure-depletion strategies to examine oil production behavior over multiple CSI cycles. Macroscopic recovery trends were analyzed alongside microscopic observations of oil displacement, gas nucleation, and foamy oil development. The results show that increasing wormhole length enhances reservoir connectivity and solvent access, resulting in a 19% improvement in the total recovery factor by 19%. Lower depletion rates favor early cycles and capillary-driven recovery, whereas higher depletion rates become more effective in later cycles as gas expansion and foamy oil-assisted mechanisms intensify. An incremental pressure-depletion strategy that exploits this transition yielded the highest cumulative recovery rate at 46.3%. These findings show that wormholes amplify the impact of pressure depletion rate during CSI by enhancing reservoir connectivity and pressure communication, thereby increasing the effectiveness of adaptive depletion strategies in post-CHOPS reservoirs.