ABSTRACT This work presents the validation of a flexible multipurpose setup for studying synthesis gas reactions under industrially relevant conditions. It enables precise control of temperature (RT‐1050°C), pressure (1–100 bar(a)), and gas composition and is equipped with multiple online detection methods (IR, GC, QMS, and Raman). Reference tests on commercial catalysts demonstrate high reproducibility (±3.2%–5.2% deviation from specialized setups). Raman spectroscopy proved capability to detect all key species, whereas IR and GC offered superior sensitivity for NH 3 and MeOH, respectively. The MP setup successfully operated NH 3 decomposition (up to 30 bar(a)) and MeOH/NH 3 synthesis (up to 50 bar(a)/100 bar(a)). Its versatility makes it a powerful tool for detailed kinetic studies in synthesis gas chemistry.
ABSTRACT Magnetic resonance (MR) allows imaging and mapping of parameters, such as flow or temperature of fluids, in optically opaque packed‐bed reactors (PBRs). Although velocity‐encoding MRI has previously been applied to the study of PBRs, most investigations have focused on liquid flow, as gas‐phase measurements remain challenging due to the low density and resulting low signal‐to‐noise ratio of gases. In this work, phase‐contrast MRI was used to map and compare qualitatively and quantitatively the flow of a hydrocarbon gas and water in fixed‐bed reactors, operated at comparable Reynolds numbers, at magnetic field strengths of 3 and 7 T. In addition, the same pulse sequence was applied in a second experimental setup to obtain temperature maps, setting the basis for future simultaneous flow and temperature measurements in optically opaque packed beds.
ABSTRACT The gas purity is a critical parameter for methanol synthesis over industrial Cu/ZnO/Al 2 O 3 catalysts. Therefore, catalyst poisons are of particular importance, since even small amounts can be sufficient to significantly reduce catalyst activity. Metal carbonyl compounds M(CO) x are detected as such poisons, which can easily be formed in CO gas lines on defective and/or unsuitable components. To prevent poisoning in laboratory tests, which would affect the results obtained and might lead to misinterpretations, the installation of carbonyl filters is required. We present a study and validation of various filter systems based on thermal decomposition and adsorption of carbonyl species.
ABSTRACT With growing global lithium demand, brine resources have gained research focus due to abundance but extraction challenges. This review summarizes lithium enrichment advances from various brines, comparing mainstream technologies. Adsorption methods show superior potential, though adsorbent performance varies significantly. We comprehensively analyze adsorbents’ technical characteristics, including chemical structures, synthesis, adsorption mechanisms, and separation performance. By evaluating advantages/limitations alongside mechanisms and application challenges, optimization strategies are proposed. Future development directions for novel adsorbents are suggested, providing theoretical and practical guidance to advance brine lithium extraction technologies.
Piping and instrumentation diagrams (P&IDs) are central engineering documents whose review remains largely manual, time-consuming and error-prone. We investigate generative artificial intelligence (GenAI) for P&ID revision. Specifically, we interpret P&ID correction as a machine translation task. Machine-readable DEXPI P&IDs are converted with pyDEXPI into attributed graphs, and corrected topologies are represented as generalized SFILES sequences (GGILES). Based on this representation, we adapt a transformer-based Graph-to-SFILES model to utility-system P&IDs and train it on a synthetic dataset of paired erroneous and corrected diagrams. On this benchmark, the model achieves high accuracy and learns attribute-dependent error patterns. Tests on five industrial DEXPI P&IDs reveal a gap between synthetic and industrial data, highlighting limitations in data availability and coverage.
ABSTRACT For inline process analysis of foams, a novel optical multiphase conductivity inline probe is presented, combining optical imaging and electrical conductivity measurements in a size‐adjustable volume. Telecentric illumination enables position‐independent detection of bubble structures, while gold‐plated electrodes determine local conductivity. This allows simultaneous determination of bubble size distribution and liquid phase fraction. Automated segmentation extracts foam structures for statistical analysis, while conductivity is correlated to liquid content based on the literature. Stirred tank experiments show strong agreement (mean absolute error 4.4%, 78% within the error band), demonstrating robust inline foam characterization under realistic conditions.
Liquid maldistribution in packed beds can significantly reduce process efficiency and is strongly influenced by the liquid distributor design. In this work, four lab-scale liquid distributors based on gravity- and pressure-driven operating principles were investigated at two liquid loads. Their performance was evaluated using flow maps and the maldistribution factor, revealing substantial differences between designs. The distributor providing the most homogeneous distribution was further analyzed regarding flow stability, gas load, and packing interaction. Compared to a single-point liquid distribution, the proposed distributor reduced maldistribution and wall flow. Magnetic resonance imaging measurements showed a progressive deterioration of the flow homogeneity along the packing height.
Abstract Stacked autoencoder (SAEs) are widely applied in industrial processes. However, issues such as insufficient correlation between hidden layer features and quality variables, feature redundancy caused by increased stacking layers, and inadequate utilization of unlabeled samples compromise prediction accuracy. This article proposes a method called semi‐supervised learning of parsimonious spatiotemporal representations for industrial quality prediction (SQTDSTF). A semi‐supervised spatiotemporal feature extraction network (SST) processes labeled and unlabeled samples; a quality‐related module selects key features to enhance feature‐quality correlation; kernel principal component analysis (KPCA) is applied to each hidden layer to suppress redundancy. The effectiveness of the SQTDSTF model is validated through simulations of debutaning towers and thermal power generation.
The steel industry contributes approx. 7%-9% to global CO2 emissions and is therefore among the most emission-intensive industrial sectors. Although hydrogen (H2) based direct reduction offers a long-term solution, transitional strategies are essential for existing assets. Internal carbon cycles describe the reuse and conversion of carbon-containing by-products within the steel plant and present a promising approach to circular economy. This article examines the mechanisms, technical feasibility, and environmental benefits of internal carbon loops and carbon capture utilization (CCU) integration, supported by recent research and industrial projects such as Carbon2Chem. Despite a growing body of CCU and steel decarbonization reviews, a consolidated discussion that starts from the realities of integrated-plant gas and energy networks (availability, quality, and operational constraints) and translates them into actionable internal carbon-cycle options remains limited. Therefore, this review provides an industrial practitioner perspective and proposes a structured classification of internal carbon cycles by gas stream, conversion step, and reintegration point in the steelworks to better distinguish near-term retrofit pathways from longer-term concepts.
Since 2016, the publicly funded project Carbon2Chem has been demonstrating that energy transition solutions can be successfully developed within an interdisciplinary network involving science and industry. In concrete terms, the project is developing technical carbon capture and utilization (CCU) solutions. The key application is the use of metallurgical gases containing CO2 from steel production to manufacture chemical products, such as methanol. Despite the constant changes in the project's framework conditions, the project, now in its third funding phase, is set to achieve its goals by the end of 2028. The project has a number of unique selling points.
Mechanical vapour recompression (MVR) offers a promising route to increase energy efficiency and cut CO2 emissions in evaporation and distillation processes by recompressing and recycling vapour streams. Yet, standardised tools for early-stage techno-economic-environmental evaluation are scarce. This contribution presents a generic assessment approach for MVR, implemented in the Python-based WindaB tool. In a methanol-water separation case study, we quantify how the reboiler temperature difference (Delta T reb) drives operational expenditures (OPEX), capital expenditures (CAPEX), payback time and CO2-equivalent emissions, and we assess sensitivities to discount rates, CO2 certificate prices and energy mixes. Moderate Delta T reb values combined with low-carbon utilities deliver both economic and ecological gains. The proposed approach thus provides a robust decision-support framework for sustainable process design.
The integration of heat pumps offers a promising route for electrifying chemical processes and reducing CO2 emissions. Their feasibility strongly depends on the temperature levels and the quantities of available heat sources and sinks, which can be influenced by adjusting process operating parameters to enhance integration potential. The number and quality of these sources and sinks also determine suitable heat pump configurations and therefore the technical and economic viability of implementation. In addition, refrigerant selection is a critical factor, as it is restricted by regulations such as the F-Gas Regulation. This study investigates how different operating parameters affect the integration potential of various heat pump configurations in a CO2 absorption process using MEA as solvent. Furthermore, economic evaluations are carried out considering different electricity price scenarios and allowable refrigerants.
This work presents the geometric optimization of an axial-radial impeller (ARI) hybrid to maximize gas-liquid mass transfer using response surface methodology. A Box-Behnken design was applied to evaluate the effects of blade number, inclination angle, and aspect ratio on the volumetric mass transfer coefficient (KLa). The optimized configuration was experimentally validated and compared with the original ARI and a Rushton turbine under fermentation-relevant conditions. Results showed an 8% increase in KLa, and a 25% reduction in power consumption relative to the original design. The optimized ARI demonstrated superior performance at medium-to-high aeration and agitation, confirming the effectiveness of systematic geometric optimization.
The persistence of pharmaceutical residues in aquatic environments presents a significant challenge for water treatment, necessitating the development of efficient and low-cost removal strategies. In this study, a chemically modified rice husk-based composite incorporating zinc oxide and treated with orthophosphoric acid and an anionic surfactant was evaluated as an adsorbent for the removal of metronidazole and tetracycline from aqueous solution. The adsorption process was systematically optimised using response surface methodology based on a Box-Behnken experimental design, considering the effects of solution pH, adsorbent dosage and initial contaminant concentration. Under optimised conditions, the modified composite achieved a maximum removal efficiency of 99.1% for metronidazole at alkaline pH with low contaminant concentration and moderate adsorbent dosage, significantly outperforming the unmodified composite.
Drum (trommel) screens are widely used to classify packaging waste by particle size. This study quantifies the size-classification behaviour of elongated, hollow packaging-like items (representing bottles, tubes and cups) using cylindrical and tubular test pieces. Experiments were conducted with a 70 mm screen opening size (often referred to as mesh size). The time to reach 90 wt% classified mass ranged from 18 to 111 s. Faster classification was observed for PET-bottle oversize particle feeds compared with mixed oversize particle feeds, for heavier (residue-filled) samples, and for samples with lower fine particle height and lower diameter. These results provide quantitative guidance for predicting and optimizing drum screening performance in packaging waste treatment.
The steel industry is considering switching from the blast furnace-basic oxygen furnace (BF-BOF) route to the hydrogen-based direct reduction (DR) route. This transformation will affect the availability of steel mill gases (SMGs) for carbon capture and utilization (CCU). This research article investigates the environmental consequences of the transformation on e-methanol produced by CCU. Non-combustion of SMG reduces climate change impacts, whereas the substitution of the lost energy output influences the other impact categories. The CCU-methanol routes are better than reference methanol in almost two-thirds of all environmental impact categories. Cleaner production of steel, copper, and concrete for renewables is imperative to achieve holistic reductions in all/majority of impact categories.
An analytical plant design model was developed to describe the quasi-stationary state of vacuum pressure swing adsorption (VPSA) process for upgrading biogas (CO2 and CH4) to biomethane (CH4). A reduced VPSA cycle comprising the steps adsorption, depressurization, and evacuation was considered, using a model biogas with 50% CO2 in 50% CH4. The mass balance for the gas phase was formulated using a simplified advection-reaction approach and for the solid phase using the Linear Driving Force (LDF) model. The steady-state model serves as a basis for process design and optimization, allowing the determination of the required bed height as a function of the target product purity.
Modular production concepts enable flexible process plants composed of standardized process equipment assemblies (PEAs). However, the practical implementation of modular plants across the entire engineering lifecycle, including planning, approval and operation, remains challenging. Within the ENPRO REUNION project, semantic PEA datasheets (SPEADs) were developed to enable manufacturer-independent and machine-readable module descriptions. The approach was validated through the commissioning of a modular pilot plant integrating modules from different manufacturers and through the development of a modular crystallization unit. In addition, energy monitoring via digital twins and a knowledge-graph-based hazard and operability study (HAZOP) analysis were implemented. The results demonstrate key technological building blocks for modular production while highlighting remaining challenges regarding semantic integration and regulatory approval.
In process industries, the failure of a single component can lead to unexpected system shutdowns. This risk becomes even greater in aging plants, where original spare parts may no longer be available. As a result, spare parts management plays a crucial role in maintaining reliability and continuous operation. Reverse engineering (RE) combined with additive manufacturing (AM) offers a solution to this challenge. This approach shortens delivery times, reduces dependency on conventional supply chains, and supports the functional restoration of critical equipment. The aim of this study is to present a process chain for spare parts reproduction in the process industry that integrates RE and AM. Emphasis is placed on meeting established quality assurance and quality control requirements. A water pump impeller is used as a representative case study to demonstrate the proposed approach and its practical applicability.
