Abstract To address the significant temporal characteristics and the scarcity of labelled data in complex industrial processes, specifically targeting the latent temporal dynamics that are difficult to observe directly during process evolution, this paper proposes a latent temporal feature dual‐head coordination network (LTFDHCN). First, a posterior generation (PG) module is constructed, utilizing a bi‐directional long short‐term memory (Bi‐LSTM) network to extract bidirectional temporal context and infer the posterior distribution in the latent space. Second, a prior prediction (PP) module is designed to establish a dynamic temporal model based on the latent variable of the previous time step. This mechanism predicts the prior distribution for the current moment, breaking the traditional assumption in variational autoencoders (VAE) that latent variables follow a static prior, thereby capturing deeper latent temporal dynamics. The Kullback–Leibler (KL) divergence is employed to minimize the discrepancy between the posterior and dynamic prior distributions, enforcing the latent space to encode temporal dependencies. Furthermore, a dual‐head coordination (DHC) strategy is proposed. Leveraging a shared latent space and decoder backbone, the model simultaneously executes unsupervised data reconstruction and supervised quality prediction. This strategy effectively overcomes the label scarcity issue by explicitly mining the latent information embedded in unlabelled data. The model is optimized via a weighted objective function that integrates KL divergence, reconstruction and prediction loss. Simulation experiments on a debutanizer column and a thermal power generation process demonstrate that the proposed method outperforms baseline methods, including VAE, VAE‐LSTM, VRNN, and temporal convolutional networks (TCN)‐VAE, verifying its effectiveness and superiority in handling complex industrial data.

Abstract The growth of the petroleum and chemical industries has increased the production of petroleum coke (PC) and oily sludge (OS), creating environmental challenges. Co‐slurry gasification of PC and OS offers a clean solution for resource utilization and harmless treatment. However, the stability mechanisms of petroleum coke‐sludge slurry (PCSS) remain poorly understood, which is key to optimizing slurry preparation and gasification. In this study, oil, solid, and water were separated from OS, and their effects on slurry rheology were analyzed. The results show that adding OS from 1 wt.% to 20 wt.% causes the apparent viscosity to first decrease and then increase by 208 mPa · s, with the lowest viscosity and best stability at 10 wt.% OS in PC. The oil phase improves flowability by lubricating and dispersing particles but increases thixotropic behaviour. The solid phase stabilizes the slurry by forming a skeletal structure and preventing sedimentation, though it increases viscosity due to internal friction. Porous sludge particles create a stabilizing network between PC particles. This study provides theoretical support for the industrial application of OS and PC co‐slurry gasification.

Abstract This study developed a 1D compartment model with 21 continuous stirred tank reactors to evaluate NH 3 co‐firing (0%–30%) with bituminous coal (BC) and Ninh Binh anthracite (NB) in a 500 MWe pulverized coal (PC) combustor, advancing low‐emission power generation for using Vietnam's coal resources. The model employs 341 reactions, including 117 nitrogen‐related, and accurately predicts combustion performance and emissions. Without NH 3 co‐firing, BC and NB show comparable combustion, with NB's higher fixed carbon (61.8 wt.% vs. 54.7 wt.%) driving faster heat release. At 10% NH 3 , NO x emissions remain stable (123.6–126.5 ppm vs. 122.3–125.5 ppm at 0%), the pathway favouring N 2 O conversion to N 2 . At 30% NH 3 , NO x increases significantly (639.8 ppm for BC, 565.7 ppm for NB) due to the NH 3 ➔ NH 2 ➔ HNO ➔ NO pathway, amplified by the BC's higher volatile matter (29.5 wt.% vs. 13.3 wt.%). CO 2 emissions decrease by 28.2% for BC and 28.0% for NB, leveraged by NH 3 's carbon‐free nature. However, increased NO x emissions at 30% NH 3 indicate trade‐offs which require further investigation. This model can be used for evaluating NH 3 co‐firing ratios in industrial‐scale combustors, balancing NO x control and CO 2 reduction while elucidating emission formation pathways.

Abstract This is the eighth article in the series ‘Survival Skills for Scientists’; previous papers were published in the Journal of Materials Education . Here I describe my views, based partly on personal experience, about pursuing an alternative career path within academia: becoming an administrator (e.g., Department Chair, Dean, Vice‐President, President, etc.), either as a temporary detour , or as a permanent shift . After spending several years of employment in a faculty position, many academics feel the need to face new challenges. For those who enjoy contributing to service and who see themselves as effective leaders, becoming an administrator may be a good choice. Herein I refer specifically to administrators in academia, where the management structure is inherently bottom‐up, as opposed to administration in government laboratories and industry, where the organization is operated through a top‐down approach. I mostly share the main lessons learned through personal experience, during my own tenure as Director. Finally, I discuss the leadership traits that I believe to be necessary and sufficient to inspire faculty and staff in your unit to go ‘above and beyond’ towards collectively and effectively pursue common goals, fostering a sense of belonging and nurturing a welcoming, positive work environment. From a topic perspective, most Departments in modern Faculties of Science or Engineering are intrinsically multidisciplinary, which requires an ability to make connections between different areas to jointly address challenges and capitalize on opportunities. Without wanting to overly generalize, these views are likely to be suitable for administrator roles in such Departments.

The cover image is based on the article Glycerol carbonate formation from glycerol and dimethyl carbonate using lithium‐based catalysts by Ajay K. Dalai et al., https://doi.org/10.1002/cjce.70120 image

Abstract The persistent demand for high‐performance, lightweight, and multifunctional materials has positioned carbon fibre‐reinforced polymer composites (CFRPCs) at the forefront of innovation across critical sectors, including aerospace, defence, civil engineering, and energy. This review provides a critical examination of how various micro‐ and nanostructured carbonaceous fillers—specifically, carbon nanofibres, carbon nanotubes (CNTs), graphene, graphite flakes, nano‐diamonds, and human hair‐derived porous carbon—are fundamentally altering the mechanical, viscoelastic, and electrical characteristics of CFRPCs. The present study reveals that these fillers, which span one‐dimensional (1D), two‐dimensional (2D), and three‐dimensional (3D) architectures, allow for highly tailored reinforcement and establish electrical percolation thresholds at remarkably low loadings. Notably, CNT‐reinforced composites have demonstrated substantial gains, achieving flexural strengths up to 580 MPa. Furthermore, this study explores the significant synergistic effects in hybrid filler systems, which consistently yield superior stiffness, damping capacity, and conductivity compared to their single‐filler counterparts, applications, challenges, and future directions. The paper also provides a comparative analysis of advanced fabrication techniques such as vacuum‐assisted resin transfer moulding, compression moulding, and 3D printing, highlighting their direct influence on scalability, fibre wet‐out quality, and overall structural integrity. Despite these clear advances, persistent challenges remain in achieving uniform filler dispersion, cost‐effective processing, and material recyclability.

Abstract The complex flow characteristics within a sodium electrolyzer directly influence electrolysis efficiency and energy consumption. In this study, a gas–liquid two‐phase flow model of a sodium electrolyzer was developed in COMSOL Multiphysics. The standard k ‐ ε turbulence model was coupled with a Euler–Euler two‐phase flow framework to qualitatively characterize the overall flow field. Cross‐sectional profiles were then extracted to elucidate internal flow behaviours, and the impacts of key process parameters on the flow field were examined. The results show that increasing anode thickness and electrode distance both reduce the gas volume fraction and electrolyte velocity in the channel; lowering the cathode height effectively suppresses localized bubble accumulation and enhances flow uniformity; and raising the electrolyte liquid‐surface height leaves the gas volume fraction unchanged while diminishing the liquid velocity in the channel.

Abstract Wastewater generated in the washing units of waste plastic recycling facilities exhibits a complex composition due to high concentrations of organic pollutants, thereby necessitating advanced treatment strategies. Consequently, identifying effective treatment technologies capable of achieving high total organic carbon (TOC) removal efficiencies for such wastewater is of critical importance. In this study, the hypothesis that a hybrid treatment approach involving the sequential application of chemical coagulation (CC) and electrooxidation (EO) could enable efficient and high‐performance TOC removal from waste plastic recycling wastewater (WPRW) was systematically evaluated. During the pretreatment stage, two coagulants were examined, among which aluminium chloride (AlCl 3 ) yielded the highest TOC removal efficiency (40.8%). Subsequently, the EO process was conducted using boron‐doped diamond (BDD) and platinum–iridium oxide (Pt–IrO 2 ) electrodes. The effects of key operational parameters, including electrode type, applied current (0.5–2 A), pH, and mixing speed (200–800 rpm), on TOC removal were investigated. Following AlCl 3 coagulation, maximum TOC removal efficiencies of 89.19% and 63.41% were achieved using BDD and Pt–IrO 2 electrodes, respectively, under optimum conditions (pH 6, 20 A/m 2 ). Energy consumption analysis revealed that higher mixing speeds allowed the use of lower current densities without compromising TOC removal, indicating substantial potential for process‐level energy optimization. The specific energy consumption (SEC) ranged from 0.01 to 0.28 kWh/kg TOC removed for the BDD electrode and from 0.03 to 0.40 kWh/kg TOC removed for the Pt–IrO 2 electrode. Overall, the BDD anode exhibited high treatment efficiency and energy economy, making AlCl 3 ‐assisted BDD electrooxidation a promising approach for plastic recycling wastewater treatment.

Abstract Efficient harvesting of microalgae is a critical challenge for the viability of biorefineries, requiring technologies that combine high selectivity, low cost, and scalability. The current work evaluated the performance of an asymmetric hollow fibre ceramic membrane (HF‐SiC70Si30), fabricated by phase inversion and sintered at 1250°C, in the harvesting of Chlorella vulgaris . The membrane exhibited a hydraulic permeability of 332 L m −2 h −1 bar −1 and 100% cell retention, attributed to its hierarchical porous structure and hydrophilicity (contact angle: 71.95°). Fouling analyses showed that cake formation was the dominant mechanism ( R 2 = 0.89), with 86.7% of the resistance being reversible, allowing 85 % of the flux to be recovered after backwashing. The low‐temperature sintering (1250°C) and mechanical robustness of HF‐SiC70Si30 highlight its potential for industrial applications, overcoming the limitations of conventional polymer and ceramic membranes.

Abstract This study introduces a data‐driven control strategy integrating hidden Markov models (HMM) and reinforcement learning (RL) to achieve resilient, fault‐tolerant operation against persistent disturbances in nonlinear chemical processes. Called hidden Markov model and reinforcement learning (HMMRL), this strategy is evaluated in two case studies for parametric disturbances in a wide range while being trained only on three operation regimes with smaller and 0% disturbances. In the first case study, HMMRL reduces mean integral absolute errors (IAEs) and offsets by 43% on an average compared to RL for disturbances outside a limiting range, delivering near offset‐free set‐point tracking with mostly smooth control actions. In the second case study, despite an exponential reaction term amplifying nonlinearity, HMMRL achieves a greater 73% reduction in IAEs and offsets for disturbances beyond a smaller limiting range, and similar set‐point tracking performance. The controlled variables in both case studies, averaging in final time derivatives, signify strong system stability and precise control, despite persistent disturbances. Thus, outside a small limiting disturbance range and up to % disturbances, HMMRL is able to effectively evaluate disturbance severity, allowing prioritized actions from RL policies. These results indicate that HMMRL shows promise for fault‐tolerant process control, meriting further study to validate its effectiveness across varied conditions.