ACS Engineering Au最新文献

High-temperature heat pumps are preferred for decarbonizing many industrial processes, but are still being adopted slowly. Major barriers to adoption are low efficiency, leading to high operational cost, and the need for custom-made designs, increasing investment cost. In this work, refrigerant mixtures are exploited to overcome these barriers for high-temperature heat pump adoption. Mixtures have been known to improve heat pump efficiency if their nonisothermal phase change is matched to heat source and sink temperature changes. Beyond that, we improve standardization by using mixture composition as an additional degree of freedom to tailor a standard heat pump designed for a specific refrigerant pair to various applications. By model-band screening of 703 refrigerant pairs across 81 combinations of heat source and sink temperature changes, we identify a maximum COP advantage of 26% for a refrigerant mixture when the maximum heat source and sink temperature changes of 40 K occur. Several mixtures are identified yielding near-optimal efficiencies across all 81 heat source and sink temperature changes. The best all-rounder mixture, diethyl ether/cyclopropane, retains, on average, 97% efficiency of the individually optimal mixtures. These findings support the development of more efficient and less costly high-temperature heat pumps, a crucial step in the heat transition.

A GEMC–NPT simulation study of the liquid–liquid extraction of levulinic acid (LA), a keto–acid, from its aqueous solution via six organic solvents has been performed at 313.15 K and 101.325 kPa to identify the optimal solvent. Continuous fractional component Monte Carlo approach (by using Brick–CFCMC) to enable efficient sampling of dense coexisting liquid phases via particle transfer moves for chemical equilibrium has been adopted. The solvent performance indicators (SPIs) are distribution coefficient (K_D), separation factor (S), and Gibbs free energies of transfer (ΔG_trans) for LA and water from the aqueous to the organic solvent-rich phase. Based on SPIs, ethyl acetate is the optimal solvent, and benzene, toluene, and xylene are ineffective. The molecular-level structure resulting from the complex interplay of interactions present has been investigated by computing the center of mass (COM)–COM radial distribution functions (RDFs) and their corresponding number integrals (NIs) in both the coexisting phases. The NIs from these RDFs for LA–LA, LA–water, solvent–water, and water–water molecules in the organic solvent-rich phase exhibit trends that are correlated with those in the SPIs for the solvents. Extent of hydrogen bonding between the hydrogen H9 in the carboxylic acid group of LA with that of the oxygen atom of the solvent, and with the oxygen O_Wof water is investigated via site–site intermolecular RDFs. The NIs from carboxylic acid group carbonyl oxygen O7 of LA–O7 RDFs including the first two peaks agree with the trends in K_D and ΔG_trans of LA for ethyl acetate, n-octanol, and 2-heptanone. Further, NI values from H9–O_W RDFs including the first and second coordination shells show trends in agreement with ΔG_trans of water.

Corrosion represents a key impediment to the greater adoption of light metal alloys as alternatives to automotive steels in vehicular applications. Thin nanocomposite coatings generate considerable interest for their potential in aluminum alloy corrosion protection, which is challenging due to the lack of conventional protection mechanisms that are available for other metals. Here, we investigate the thickness-dependent corrosion protection afforded to AA 7075 substrates by poly(ether imide)-based (PEI) coatings. Using electrochemical impedance spectroscopy to monitor ion transport, we observe that with increasing coating thickness, PEI more effectively sequesters ions and enforces permeation selectivity, thereby precluding deleterious substitution processes that dissolve corrosion products. We further explore thickness-dependent modifications to the PEI matrix by incorporation of unfunctionalized exfoliated graphite (UFG) particles to control diffusion processes and co-polymerization with siloxane to manipulate permeation selectivity. Incorporation of UFG platelets can degrade corrosion protection through galvanic coupling with the substrate and enhanced interfacial ion diffusion at lower coating thicknesses. However, interphase development mediated by hydration, network relaxation, and thermal displacement of PEI chains yields a rigid matrix that enhances permeation selectivity and imbues extended tortuosity. This combination results in superior corrosion protection for thicker PEI coatings with embedded UFG platelets under aggressive accelerated corrosion testing conditions. Siloxane co-polymerization, while weakening interfacial adhesion to AA 7075 substrates, facilitates the sequestration of solubilized corrosion products within the matrix under appropriate processing conditions. The results illustrate the importance of understanding the dynamical evolution of polymer secondary structure under aggressive accelerated corrosion testing conditions, point to the specific role of secondary structure and interphasic domains in enforcing permeation selectivity, and establish fundamental thickness limits for retaining effective barrier protection.

Advances in sensing technologies and AI have resulted in new in-line and online process measurements based on video, vibration, chromatograms, and other high-dimensional data that can complement common process measurements such as pressure, temperature, and flow rates. These sensors can be beneficial for process monitoring; however, their continuous use is often highly expensive or even impractical. In this work, we propose a novel fusion strategy to integrate insights from these sources when needed while predominantly relying on the less expensive common measurements. A hierarchical organization of sensors based on a generalized cost metric serves as the basis for the fusion. The fusion process intelligently utilizes the least expensive data first. Costlier data are used by the fusion scheme only if found necessary in real-time to improve performance. Through this lazy fusion strategy, heterogeneous multimodal sensors can be utilized within a unified framework to improve decision timeliness, accuracy, and reliability while being robust to data delays, sensor failures, and computational limitations. The proposed fusion technique has been tested on two case studies, a simulated CSTR process and an experimental data set obtained from a multiphase flow facility. The obtained results show a significant reduction in diagnostic delay compared to traditional process monitoring while utilizing costly video and high-frequency measurements only 15–30% of the time.

Identifying active sites and their roles in chemical reaction steps remains a vital challenge in heterogeneous catalysis. Transient experiments offer a unique way to probe active sites and distinguish subtle kinetic features. Although physics-based analysis methods may be well-developed, they can be highly susceptible to experimental noise, and smoothing methods may erase or even distort important features; a smooth curve is not always the best curve. We demonstrate a new workflow for the direct interpretation of intrinsic kinetic information from exit flux curves measured in transient reactor experiments. This workflow contains three artificial neural networks (ANNs), including a noise reducer, a concentration predictor, and a rate predictor to analyze experimental data, followed by the virtual TAP (VTAP) physics-based reactor model and density functional theory (DFT) calculations of adsorption energies on specific sites. We use this workflow to analyze the data from experiments titrating Pt/Al₂O₃ and Pt/SiO₂ catalysts with carbon monoxide (CO) in the temporal analysis of products (TAP) reactor. Our workflow separates the time-evolving chemical reaction and mass transfer information contained in the TAP pulse response. The existence of strong- and weak-binding sites on the Pt/Al₂O₃ catalyst is observed in the catalyst titration experiment in the transient reactor. The structures of the strong- and weak-binding sites are then identified by using DFT calculations. We find that the Pt/SiO₂ catalyst has only strong-binding sites, which aligns with the inactive support effect of SiO₂. We demonstrate how machine learning methods provide unique insights with high-resolution data analysis that cannot be achieved by using state-of-the-art physics-based methods.

The development of effective synthetic pathways is critical in many industrial sectors. The growing adoption of flow chemistry has opened new opportunities for more cost-effective and environmentally friendly manufacturing technologies. However, the development of effective flow chemistry processes is still hampered by labor- and experiment-intensive methodologies and poor or suboptimal performance. In this context, integrating advanced machine learning strategies into chemical process optimization can significantly reduce experimental burdens and enhance overall efficiency. This paper demonstrates the capabilities of deep reinforcement learning (DRL) as an effective self-optimization strategy for imine synthesis in flow, a key building block in many compounds such as pharmaceuticals and heterocyclic products. A deep deterministic policy gradient (DDPG) agent was designed to iteratively interact with the environment, the flow reactor, and learn how to deliver optimal operating conditions. A mathematical model of the reactor was developed based on new experimental data to train the agent and evaluate alternative self-optimization strategies. To optimize the DDPG agent's training performance, different hyperparameter tuning methods were investigated and compared, including trial-and-error and Bayesian optimization. Most importantly, a novel adaptive dynamic hyperparameter tuning was implemented to further enhance the training performance and optimization outcome of the agent. The performance of the proposed DRL strategy was compared against state-of-the-art gradient-free methods, namely SnobFit and Nelder-Mead. Finally, the outcomes of the different self-optimization strategies were tested experimentally. It was shown that the proposed DDPG agent has superior performance compared to its self-optimization counterparts. It offered better tracking of the global solution and reduced the number of required experiments by approximately 50 and 75% compared to Nelder-Mead and SnobFit, respectively. These findings hold significant promise for the chemical engineering community, offering a robust, efficient, and sustainable approach to optimizing flow chemistry processes and paving the way for broader integration of data-driven methods in process design and operation.

[This corrects the article DOI: 10.1021/acsengineeringau.4c00025.].

Multiphase reactors (MPRs) are crucial in converting raw materials into essential products such as chemicals, polymers, and medicines and contribute immensely to the global economy. MPRs are complex dynamical systems involving chemical reactions and interphase transport processes. State-of-the-art designs of MPRs often struggle to deliver materials and energy precisely at the right time and place in the reactor, leading to unwanted side products and excess energy consumption. This is mainly due to our inability to accurately predict and direct the flow of materials and energy within MPRs. In this Perspective, I propose a novel way of developing high-fidelity models of MPRs by synergistically combining wall pressure fluctuation data acquired from these MPRs with machine learning and physics-based models. This novel approach has the potential to capture multiscale information contained in pressure fluctuations and thereby deliver unprecedented accuracy to MPR models. This will enhance their fidelity and applicability to real-world reactors without needing resolution of micro- and mesoscales or using any ad hoc adjustments. The novel methodology is discussed by considering a case of bubble column reactor as a representative MPR. Evidence available in the published studies that lends support to the key hypothesis underlying the proposed methodology is briefly discussed. Specific suggestions on how to develop and validate the proposed approach are included. The proposed approach will lead to high-fidelity models anchored to real-world reactors via wall pressure fluctuations and thereby facilitate the identification and implementation of optimal strategic interventions to influence the multiphase transport in MPRs. This will ensure precise delivery of materials and energy and thereby eliminate side products and minimize energy consumption. I believe that it will transform the foundations of simulating and intensifying MPRs, leading to significantly better resource utilization and reduced emissions in the future.