Industrial & Engineering Chemistry Research最新文献

Hybrid modeling frameworks that integrate first-principles equations with data-driven corrections offer a promising approach for capturing complex, partially known system dynamics. However, ensuring the stability and physical realism of the learned correction terms remains a significant challenge, especially when using flexible function approximators such as neural networks. This work introduces a Lyapunov-constrained hybrid modeling framework that ensures the boundedness of a learned parameter dependent on the states. Leveraging tools from nonlinear control theory, we perform a Lie derivative analysis to extract relative-degree features, construct a reference map for the unknown parameter, and embed a Lyapunov decay constraint directly into the model’s training objective. We validate our method on a benchmark CSTR system with time-varying fouling, where the heat transfer coefficient is treated as an unmeasured and drifting parameter. Results show that the proposed hybrid model accurately reconstructs system trajectories and parameter profiles while enforcing bounded deviation from the precomputed reference manifold. The Lyapunov-based penalty ensures that the correction term remains physically plausible and stable across all operating conditions, even in open-loop simulations. This framework provides a rigorous foundation for deploying hybrid models in safety-critical process systems.

Herein, we report an optimized synthetic pathway and application of S-functionalized 3,7,10-trimethylsilatrane derivatives (Sil69, Sil266, and SilNXT) as silatrane coupling agents (SilCAs) dedicated to preparation of SSBR/BR/SiO₂ composites. The tire performance of silatrane-based elastomers was compared to reference samples prepared with commercial triethoxysilyl compounds─Si 69, Si 266, and NXT. We discovered that the melting point of SilCAs can critically affect the degree of silica surface coverage during rubber mixing, as indicated by bound rubber content and the Payne effect. Furthermore, we proved vulcanization accelerating properties of the silatranyl moiety, as well as trialcoholamine through model reactions. The unique chemistry of the cage-shaped silica-binding group allowed us to achieve with SilNXT, 14% greater tensile strength, 8% higher wet traction, and comparable rolling resistance and abrasion resistance, in reference to NXT.

Multivariate statistical methods are important for establishing the relationship between variables in a dynamic process. However, attention is rarely paid to utilizing prior knowledge and identifying the degree of process dynamics while extracting latent variables (LVs), which are vital in practice for improving modeling efficiency. In this article, a novel nonparametric dynamic inner LV regression algorithm is proposed for process modeling by extracting explicit dynamic LVs from highly correlated process and quality data. The inner-dynamic structure between LVs is modeled as an impulse response, where the model order defines the degree of dynamics in the process. The estimation of the inner model with a nonparametric system identification technique enables the model to achieve a good balance between model complexity and flexibility, thus realizing the estimation of the degree of dynamics in the process and ensuring the consistency of model performance. Prior knowledge of the impulse response is incorporated by a kernel-based regularization technique while searching for the inner model within an infinite-dimensional space to enhance the smoothness and stability of the model. Besides, prior knowledge is integrated into the progress of dynamic LV extraction as well, thereby improving the numerical properties of the dynamic LV regression model. Case studies based on chemical and biological processes are presented to demonstrate the effectiveness of the proposed method.

In this study, a series of vinyl-rich butadiene–isoprene copolymers was synthesized using a commercially available cobalt naphthenate/triisobutylaluminum/carbon disulfide/water catalytic system. Incorporation of isoprene lowered the melting point and enabled a continuous transition from semicrystalline plastics to amorphous elastomers, improving the processability of high-vinyl 1,2-polybutadiene. The copolymer sequence structure was elucidated in detail by 1D and 2D NMR spectroscopy, and their microstructure, morphology, and mechanical properties were systematically characterized. The results showed that higher 1,2-polybutadiene content increased the modulus, low isoprene contents enhanced toughness and strength (tensile strength up to 13.5 MPa), and high isoprene contents imparted rubber-like behavior with favorable dynamic viscoelasticity.

This study reports the development of bifunctional catalysts for the selective conversion of glycerol into propylene glycol (PG) via catalytic transfer hydrogenolysis (CTH) coupled with in situ hydrogen provision via aqueous-phase reforming (APR). A structured four-stage experimental framework was adopted, comprising catalyst support screening, active metal composition optimization, reusability evaluation, and kinetic analysis. Screening of natural clay minerals (alumina, montmorillonite, kaolinite, Illite, and attapulgite) under APR–CTH conditions (250 °C, 6 h, and 20% v/v glycerol) identified alumina as the most effective support, owing to its amphoteric surface properties, strong metal–support interactions, and structural stability. Subsequent optimization using an augmented simplex centroid design revealed Cu–Mg/Al₂O₃ as the optimal formulation, achieving 86.16% glycerol conversion and 43.81% PG yield, while minimizing byproduct formation. Notably, Ni was excluded from the optimum composition due to its negative effect on PG selectivity and tendency to promote unselective hydrogenolysis. The optimized Cu–Mg/Al₂O₃ catalyst exhibited stable performance across ten consecutive cycles, maintaining a turnover frequency of 8.17 h^–1 and a turnover number of 49.01 mol product per mol active site, underscoring its robustness and reusability. Kinetic modeling using a Langmuir–Hinshelwood framework revealed that glycerol reforming and hydrogenolysis to PG occurred primarily on basic sites with moderate activation energies (29.49–40.18 kJ·mol^–1), while undesired PG hydrogenolysis to 2-propanol was confined to acidic sites with a significantly higher barrier (219.78 kJ·mol^–1). This work establishes Cu–Mg/Al₂O₃ as a promising bifunctional catalyst and provides mechanistic and kinetic insights that advance the sustainable valorization of glycerol into PG, aligning with Sustainable Development Goals 12 and 13.

This study proposed a simplified process in which FeCl₃ was directly dissolved in actual acidic salt-lake brine, in contrast to the conventional tributyl phosphate (TBP)/FeCl₃ extraction system that requires predissolving FeCl₃ in an acidic, saturated MgCl₂ solution prior to mixing with TBP to prepare the Fe³⁺-loaded organic phase. This modification streamlined the operational procedure and significantly reduced the rate of reagent consumption. Under optimized conditions (organic-to-aqueous phase ratio = 2, 70 vol % TBP, Fe/Li molar ratio = 1.5), the process achieved a lithium-extraction efficiency of 93.34%. The extraction mechanism of Li⁺, which involves cation exchange and coordination between TBP and Li⁺, was elucidated by Fourier-transform infrared spectroscopy and nuclear magnetic-resonance analysis. Furthermore, lithium carbonate with a purity of 97.48% was obtained from the stripping solution. Overall, this work provides a practical basis for the design and optimization of lithium extraction from salt-lake brine with high Mg/Li ratios.

Motorcycle oxidation catalysts can reduce urban CO and VOC emissions by over 80%, but their catalytic performance progressively degrades due to hydrothermal aging and active metal sintering. This work presents a kinetic study of CO and propylene oxidation over fresh and hydrothermally aged Pt–Pd-based motorcycle catalysts with a wiremesh structure, using a global kinetic modeling approach. Incorporating surface intermediate formation and desorption successfully reproduced the characteristic two-step CO light–off behavior in the presence of propylene, in good agreement with experimental data (R² > 0.92). Despite notable variations in experimentally determined activation energies under different oxygen contents, a single set of kinetic parameters─scaled with catalytic surface area─accurately described the oxidation reactions for both fresh and aged catalysts. This finding indicates that hydrothermal aging primarily reduces the available active surface area rather than altering the intrinsic reaction pathways. An empirical polynomial correlation between normalized activity and aging temperature predicted a rapid activity loss exceeding 50% between 750 and 900 °C, revealing substantial deactivation in the early stages of aging. Aging model results further showed that after hydrothermal treatment at 920 °C under reducing conditions and 980 °C under oxidizing conditions, the catalyst activity stabilized, with no further deactivation observed.

This paper presents an industrial perspective on implementing real-time optimization (RTO), model predictive control (MPC), nonlinear model predictive control (NMPC), and power scheduling in chemical and power plants. It emphasizes integrating process knowledge with mathematical programming for sustained value creation and operational excellence. Three important classes of problems are examined with an emphasis on attaining process objectives in an industrial setting with measured and unmeasured disturbances. Process modeling details are chosen based on plant objectives and available process information for sustained value creation. Mathematical programming techniques and approximations are made to ensure global optimality and robustness. Solution implementation details are chosen after taking process characteristics and distributed control system (DCS) for plant into account.

Data reconciliation adjusts noisy measurements from plants using a known process model. Traditional methods for the data reconciliation problem assume a perfect model. This assumption does not hold in many situations. This work proposes a Symmetric Kullback–Leibler Divergence (SKLD)-based Sensor Placement Design (SPD) for data reconciliation under model uncertainty. The uncertainties are modeled as bounded or stochastic uncertainties for two scenarios: uncertainty only during design or during both design and operation. The resulting integer programming SPD problems are reformulated as computationally tractable problem formulations using semidefinite programming. The approach is demonstrated on two case studies: (i) Go Yang water distribution network and (ii) Steam metering network, which show that neglecting model uncertainty results in suboptimal placements and reduced accuracy of estimates.

In this study, lignin was extracted and isolated from agricultural waste mustard stalks by using an alkaline method. Subsequently, the isolated lignin polymers were chemically crosslinked using epichlorohydrin to produce hydrogels. The viscosity-average molecular weight of the extracted lignin was determined to be 27,725 g/mol using the Mark–Houwink–Sakurada equation. The lignin hydrogel with 6 g of lignin and 1 mL of ECH exhibited a swelling index of 253% and a gel fraction of 57%. Rheological analysis also confirmed the formation of lignin hydrogel with a larger storage modulus than loss modulus. The Young modulus, tensile strength, and breakpoint were observed to be ∼1005 Pa, ∼141 Pa, and ∼10%, respectively. When incorporated into soil, this hydrogel extended water retention by up to 10 days compared to the control. As a proof of concept, potassium was loaded into the lignin hydrogel via an in situ method, and its release behavior was evaluated using various kinetic models. The Peppas–Sahlin model provided the best fit, with a correlation coefficient (R²) of 0.99, indicating that potassium release followed Fickian diffusion as the dominant mechanism. Biodegradation studies revealed a residual dry mass of 82.74% after 60 days of being buried in soil, confirming partial degradability. Furthermore, phytotoxicity tests demonstrated that the hydrogel was nontoxic to plants. Therefore, lignin-based hydrogels present a sustainable solution for agriculture, combining efficient water retention and controlled nutrient release to enhance crop growth, particularly in arid regions. This approach not only adds value to biomass but also contributes to circular bioeconomy efforts aimed at reducing the environmental impact and enhancing resource efficiency in modern agriculture.