ACS Engineering Au最新文献

Plastic production has steadily increased worldwide at a staggering pace. The polymer industry is, unfortunately, C-intensive, and accumulation of plastics in the environment has become a major issue. Plastic waste valorization into fresh monomers for production of virgin plastics can reduce both the consumption of fossil feedstocks and the environmental pollution, making the plastic economy more sustainable. Recently, the chemical recycling of plastics has been studied as an innovative solution to achieve a fully sustainable cycle. In this way, plastics are depolymerized to their monomers or/and oligomers appropriate for repolymerization, closing the loop. In this work, PET was depolymerized to its bis(2-hydroxyethyl) terephthalate (BHET) monomer via glycolysis, using ethylene glycol (EG) in the presence of niobia-based catalysts. Using a sulfated niobia catalyst treated at 573 K, we obtained 100% conversion of PET and 85% yield toward BHET at 195 °C in 220 min. This approach allows recycling of the PET at reasonable conditions using an inexpensive and nontoxic material as a catalyst.

Chemical recycling via thermal processes such as pyrolysis is a potentially viable way to convert mixed streams of waste plastics into usable fuels and chemicals. Unfortunately, experimentally measuring product yields for real waste streams can be time- and cost-prohibitive, and the yields are very sensitive to feed composition, especially for certain types of plastics like poly(ethylene terephthalate) (PET) and polyvinyl chloride (PVC). Models capable of predicting yields and conversion from feed composition and reaction conditions have potential as tools to prioritize resources to the most promising plastic streams and to evaluate potential preseparation strategies to improve yields. In this study, a data set consisting of 325 data points for pyrolysis of plastic feeds was collected from the open literature. The data set was divided into training and test sub data sets; the training data were used to optimize the seven different machine learning regression methods, and the testing data were used to evaluate the accuracy of the resulting models. Of the seven types of models, eXtreme Gradient Boosting (XGBoost) predicted the oil yield of the test set with the highest accuracy, corresponding to a mean absolute error (MAE) value of 9.1%. The optimized XGBoost model was then used to predict the oil yields from real waste compositions found in Municipal Recycling Facilities (MRFs) and the Rhine River. The dependence of oil yields on composition was evaluated, and strategies for removing PET and PVC were assessed as examples of how to use the model. Thermodynamic analysis of a pyrolysis system capable of achieving oil yields predicted using the machine-learned model showed that pyrolysis of Rhine River plastics should be net exergy producing under most reasonable conditions.

We provide strong evidence that the amounts of phenolic aldehydes (vanillin and p-hydroxybenzaldehyde, pHB) selectively released during rapid ozonolysis of grass lignins are correlated with the unsubstituted aryl carbons of lignin–carbohydrate complexes present in these lignins. In the case of acetosolv lignin from corn stover, we observed a steady yield of vanillin and pHB (cumulatively ∼5 wt % of the initial lignin). We demonstrate the continuous ozonolysis of the lignin in a spray reactor at ambient temperature and pressure. In sharp contrast, similar ozonolysis of acetosolv lignin from corn cobs resulted in a twofold increase in the combined yield (∼10 wt %) of vanillin and pHB. Structural analysis with ¹H–¹³C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance revealed that signals assigned to unsubstituted aryl carbons of lignin–carbohydrate complexes are quantitatively correlated to phenolic aldehyde production from spray ozonolysis. The ratios of the integrated peak volumes corresponding to coumarates and ferulates in the HSQC spectra of cob and corn stover lignins (SLs) are 2.4 and 2.0, respectively. These ratios are nearly identical to the observed 2.3-fold increase in pHB and 1.8-fold increase in vanillin production rates from corn cob lignin compared to corn SL. Considering that the annual U.S. lignin capacity from these grass lignin sources is ∼60 million MT, the value creation potential from these flavoring agents is conservatively ∼$50 million annually from just 10% of the lignin. These new insights into structure/product correlation and spray reactor characteristics provide rational guidance for developing viable technologies to valorize grass lignins.

The diversification of coal for its sustainable utilization in producing liquid transportation fuel is inevitable in countries with huge coal reserves. Gasification has been contemplated as one of the most promising thermochemical routes to convert coal into high-quality syngas, which can be utilized to produce liquid hydrocarbons through catalytic Fischer–Tropsch (F-T) synthesis. Liquid transportation fuel production through coal gasification could help deal with environmental challenges and renewable energy development. The present study aims to develop an equilibrium model of a downdraft fixed-bed gasifier using Aspen Plus simulator to predict the syngas compositions obtained from the gasification of high-ash low-rank coal at different operating conditions. Air is used as a gasifying agent in the present study. The model validation is done using published experimental and simulation results from previous investigations. The sensitivity analysis is done to observe the influence of the major operating parameters, such as equivalence ratio (ER), gasification temperature, and moisture content (MC), on the performance of the CL-RMC concerning syngas generation. The gasification performance of CL-RMC is analyzed by defining various performance parameters such as syngas composition, hydrogen-to-carbon monoxide (H₂/CO), molar ratio, syngas yield (Y_Syngas), the lower heating value of syngas (LHV_Syngas), cold gas efficiency (CGE), and carbon conversion efficiency (CCE). The combined effects of the major operating parameters are studied through the response surface methodology (RSM) using the design of experiments. The optimized condition of the major operational parameters is determined for a target value of a H₂/CO molar ratio of 1 and the maximum CGE and CCE using the multiobjective optimization approach. The high-degree accurate regression model equations were generated for the H₂/CO molar ratio, CGE, and CCE using the variance analysis (ANOVA) tool. The optimal conditions of the major operating parameters, i.e., ER, gasification temperature, MC for the H₂/CO molar ratio of 1, and the maximum CGE and CCE, are found to be 0.5, 655 °C, and 16.36 wt %, respectively. The corresponding optimal values of CGE and CCE are obtained as 22 and 16.36%, respectively, with a cumulative composite desirability value of 0.7348. The findings of the present investigation can be decisive for future developmental projects in countries concerning the utilization of high-ash low-rank coal in liquid fuel production through the gasification route.

Thermal control devices like diodes, regulators, and switches are essential to achieve directional heat flow for numerous applications, such as electronic systems, energy conversion or storage systems, and equipment for buildings. These devices exhibit a controllable thermal conductance that can be manipulated to allow preferential thermal transport. While several design concepts have existed for decades, they are rarely deployed due to some basic practical limitations related to scalability, cost, operating temperature, and/or requirements for external excitation. In this study, we achieved a fundamental breakthrough in developing a passive thermal switch, which has a simple and scalable design, is thermally driven (thus does not require an external stimulus), and exhibits a rectification ratio of 17.5, which is among the highest value reported for passive switches in the literature. Notably, the switch transitions from an effective thermal conductivity of ∼1.6 W/m-K (insulator) in the OFF state to ∼28 W/m-K (conductor) in the ON state near 50 °C. To demonstrate the cost-effective implementation of our technology at a large scale, we developed a self-regulating insulation panel that automatically varies its thermal resistance by using just a few thermal switches occupying less than 10% of the total surface area. Lastly, using a parametric analysis, we establish a promising pathway to further improve the performance and versatility of the proposed technology.

The axial temperature profile of a packed bed is often measured by thermocouples placed either directly in the bed or inside a thermowell centered in the reactor tube. Quantifying the impact of the thermocouple well on fluid flow, heat transport, and consequently on the measured temperatures is still an unresolved challenge for lab-scale reactors but especially, and even more so for multitubular reactors in industry. Particle-resolved computational fluid dynamics (PRCFD) simulations are a suitable approach to investigate the changes in transport phenomena exerted by inserting thermocouple wells into packed beds because they take into account the local packed bed structures. In this study, PRCFD simulations are performed based on design of simulation experiments (DoSE). The effect of the thermowell diameter and its thermal conductivity on the deviations between packed beds with and without thermowells is statistically quantified for characteristic integral quantities like pressure drop and tube wall-bed Nusselt number. The axial temperature profiles inside the thermowells can be computed efficiently with reasonably accuracy applying the Nusselt number correction as derived in this study from the DoSE in a one-dimensional pseudo-homogeneous energy balance.

Amine resins are frequently studied to capture CO₂ from industrial emission sources and air. Polyethylene imine (PEI) is a typical example showing relatively high CO₂ uptake and not too energy demanding desorption of CO₂. For practical application, its oxidation stability is of great importance. In this DFT study, the ultimate oxidation stability of the two forms of PEI, linear PEI (LPEI) and branched PEI (BPEI), is investigated. First, the oxidation stability order for amines was determined using small amine clusters: primary > secondary > tertiary amines. Using LPEI and BPEI structure-related clusters, it turned out that under optimal conditions, the formation of α-amino hydroperoxide of PEI is the rate-determining step. Optimal conditions are the total absence of initiators like transition-metal ions, NO_x, O₃, or hydrocarbons and the presence of H₂O and CO₂. All computational results are in line with experimental results.

Bioinspired silica (BIS) has received unmatched attention in recent times owing to its green synthesis, which offers a scalable, sustainable, and economical method to produce high-value silica for a wide range of applications, including catalysis, environmental remediation, biomedical, and energy storage. To scale-up BIS synthesis, it is critically important to understand how mixing affects the reaction at different scales. In particular, successful scale-up can be achieved if mixing time is measured, modeled, and kept constant across different production scales. To this end, a new image analysis technique was developed using pH, as one of the key parameters, to monitor the reaction and the mixing. Specifically, the technique involved image analysis of color (pH) change using a custom-written algorithm to produce a detailed pH map. The degree of mixing and mixing time were determined from this analysis for different impeller speeds and feed injection locations. Cross validation of the mean pH of selected frames with measurements using a pH calibration demonstrated the reliability of the image processing technique. The results suggest that the bioinspired silica formation is controlled by meso- and, to a lesser extent, micromixing. Based on the new data from this investigation, a mixing time correlation is developed as a function of Reynolds number─the first of a kind for green nanomaterials. Further, we correlated the effects of mixing conditions on the reaction and the product. These results provide valuable insights into the scale-up to enable sustainable manufacturing of BIS and other nanomaterials.

Non-thermal plasma (NTP) catalysis is a promising technology for CO₂ valorization with renewable H₂, in which catalyst design is one of the key aspects to progress the hybrid technology. Herein, bimetallic NiCo supported on CeO₂ catalysts, that is, NiCo/CeO₂, were developed with less metal loading of ∼2 wt % using mechanochemical synthesis for NTP-catalytic CO₂ methanation. During the synthesis, different addition orders of Ni and Co precursors were investigated, and the results show that the NiCo₁/CeO₂-I catalyst (which was prepared by the simultaneous addition of Ni and Co precursors, protocol I) exhibited the highest CO₂ conversion (∼60%) and CH₄ selectivity/yield (∼80%/∼50%), whereas the NiCo₁/CeO₂-II and NiCo₁/CeO₂-III catalysts (prepared by sequential addition protocols of II and III) showed very poor catalytic performance. Characterization results suggested that in protocol I, Ni and Co prefer to alloy, and concentrated oxygen vacancies on the CeO₂ surface and high surface basicity are retained as well. Such properties of NiCo₁/CeO₂-I were responsible for CO₂ activation and hydrogenation under NTP conditions, which was explained by the proposed mechanisms.

Breakthroughs in computing have led to development of new generations of Earth Systems Models providing detailed information on how our planet may locally respond to the ongoing global warming. Access to such climate information systems presents an unprecedented opportunity for engineers to make tangible contributions to climate adaptation through integration of climate information in their products, designs, and services. We introduce the concept of “Climate Informed Engineering” (CIE) as an emerging interdisciplinary field integrating climatic considerations in engineering products and services. The concept behind CIE is to enable engineers to build infrastructure, devices, sensors or develop new materials and processes that are informed by climate and climate change information. We believe CIE will be an increasingly important dimension of Engineering Science resonating with engineers and scientists with different backgrounds.