Nitrates from agricultural wastewater are harmful to human health and result in eutrophication. Several emerging electrochemical technologies have been developed independently to enable efficient recovery and recycling of nitrate waste; however, it remains unclear whether the implementation of such combined technologies can be economically viable. Herein, we perform a technoeconomic and global warming potential analysis on several hypothetical nitrate capture and conversion (NCC) systems for the recovery of nitrates from agricultural wastewater and conversion of nitrate to ammonia. The energy efficient technologies incorporated include: electrodialysis for nitrate separation, electrocatalysis for ambient ammonia production, and agrophotovoltaics as a clean energy source. Our technoeconomic analysis reveals that despite advancements in nitrate separation and conversion, capital investments for system installation cannot be recovered by the financial benefit of on-site fertilizer production. Our analysis highlights the necessity of government intervention to promote nitrate abatement technologies to ensure environmental compliance and protect public health.

Herein, we propose a novel method to enhance the photoreactivity of an MOF catalyst by grafting isocyanate bonds (−N=C=O) and sulfhydryl-complexed copper (−SCu) onto ZIF-8 (NIF-SCu). The grafting process intercalated interlayer bands between the conduction and valence bands of ZIF-8, thereby providing a “ladder” for facile electron transition. The extreme improvement in the photoreactivity of NIF-SCu could be attributed to the enhancement in light responses in the range of 350–450 nm by −N=C=O groups and the widening of the visible light range of the MOF by −SCu groups. The formation of staggered energy levels in NIF-SCu could also narrow the band gap, lower the resistance, and facilitate the transfer of photogenerated carriers, thereby generating electrons with strong reduction potential in the −SCu conduction band. This study provides a new strategy for improving or even endowing the photoactivity of environmental functional materials with wide bandgaps.

While decomposition techniques in mathematical programming are usually designed for numerical efficiency, coordination problems within enterprise-wide optimization are often limited by organizational rather than numerical considerations. We propose a ‘data-driven’ coordination framework which manages to recover the same optimum as the equivalent centralized formulation while allowing coordinating agents to retain autonomy, privacy, and flexibility over their own objectives, constraints, and variables. This approach updates the coordinated, or shared, variables based on derivative-free optimization (DFO) using only coordinated variables to agent-level optimal subproblem evaluation ‘data’. We compare the performance of our framework using different DFO solvers (CUATRO, Py-BOBYQA, DIRECT-L, GPyOpt) against conventional distributed optimization (ADMM) on three case studies: collaborative learning, facility location, and multi-objective blending. We show that in low-dimensional and nonconvex subproblems, the exploration-exploitation trade-offs of DFO solvers can be leveraged to converge faster and to a better solution than in distributed optimization

This work develops a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models for nonlinear systems switched between multiple operating regions following a prescribed switching schedule. Specifically, an RNN model is initially developed offline to model process dynamics using the historical operational data collected in a small region around a certain steady-state. After the system is switched to another operating region under a Lyapunov-based MPC with suitable constraints to ensure satisfaction of the prescribed switching schedule policy, RNN models are updated using real-time process data to improve closed-loop performance. A generalization error bound is derived for the updated RNN models using the notion of regret, and closed-loop stability results are established for the switched nonlinear system under RNN-based MPC. Finally, a chemical process example with the operation schedule that requires switching between two steady-states is used to demonstrate the effectiveness of the proposed RNN-MPC scheme.

Microbial processes sour oil, corrode equipment, and degrade hydrocarbons at an annual global cost to the oil and gas industry of nearly $2 billion. However, top-down control of these microbial processes can reduce their damage and enhance oil recovery. Here, we screened microbial communities from five oil wells in the Illinois basin and evaluated nutrient injection strategies to control metabolism and community composition. Molasses and molybdate supplementation stimulated significant gas and organic acid production while completely suppressing corrosive H2S formation in samples from two wells. These changes were accompanied with significant shaping of the microbiome community. Simulations of field operations via a lab-scale mini-coreflood validated that oil well microbiomes can be engineered to also shape oil hydrocarbon composition in situ. These pilot studies validate the potential of economical and sustainable top-down microbiome engineering to control microbes in oil extraction and enhance the economic viability of oil recovery.

In this work, stationary states in nonequilibrium plasmas of chemical reactions that can produce hydrogen are explored, namely the water splitting and water gas shift reactions. For both reactions, the effluent from the reactor at long gas residence times in the plasma was found to be independent of the influent speciation. In other words, feeding the reactor either 0.1 H2O or 0.1H2+0.05O2 by mole produced the same effluent composition, and similarly, feeding the reactor 0.1CO+0.1H2O produced nominally the same effluent as 0.1CO2+0.1H2. For both reactions, the effluent from the plasma was found to be very far from local equilibrium at the total pressure and background temperature of the reactor. An important conclusion of this work is that special attention must be paid to the recombination zone in plasma chemical processes. The recombination zone tends to drive the gas composition from plasma stationary states back towards local equilibrium.

An economical and highly uranium extraction from seawater remains a crucial task for energy sources and environmental safety. Aiming for improving the mass transfer rate of uranium from seawater, a new synthetic strategy was adopted to synthesize 2D-open channel microporous bio-adsorbent for uranium extraction from seawater. Herein, a vapor phase modification approach was adopted to graft divinylbenzene(DVB), and polyacrylonitrile(AN) onto the surfaces of microporous frameworks via a free radical polymerization method. The post-synthetic functionalization was carried out by hydrothermal process, where amidoxime groups are structure-directing agents to trap uranium. Further, amidoxime groups not only enhanced hydrophilicity but also adjusts adsorbents pKa. AO-Fc faces minimum interference of competing ions and achieves a high uranium adsorption capacity of 8.57±0.02 and 409±1 mg/g in seawater and simulated solution. Despite its stable structure, AO-Fc exhibits a long life span and negligible weight loss revealed AO-Fc could be applied as a potential adsorbent for radionuclides

The production of hydrocarbons for the synthesis of readily available energy and multifunctional materials is of great importance in modern society. Zeolites have proven to be a boon for the targeted regulation of specific hydrocarbon as shape-selective catalyst in converting carbon resources. Yet our mechanistic understanding and quantitative description of shape-selectivity of zeolite catalysis remains rather limited, which restricts the upgrade of zeolite catalysts. Herein, we proposed quantitative principle of shape-selectivity for zeolite catalysis using methanol-to-hydrocarbons (MTH) as model. Combining with molecular simulations and infrared imaging, we unveil the competition of thermodynamic stability, preferential diffusion and favored secondary reactions between different hydrocarbons within zeolite framework are the essence of zeolite shape-selective catalysis. Notably, we provide methodology to in silico search for the optimal combination of framework topology and acidity properties of zeolites with operating conditions that potentially outperform commercial MTH catalysts to achieve high selectivity of desired hydrocarbon products.

While protein medications are promising for treatment of cancer and autoimmune diseases, challenges persist in terms of development and injection stability of high-concentration formulations. Here, the extensional flow properties of protein-excipient solutions are examined via dripping-onto-substrate (DoS) extensional rheology, using a model ovalbumin protein (OVA) and biocompatible excipients polysorbate 20 (PS20) and 80 (PS80). Despite similar PS structures, differences in extensional flow are observed based on PS identity in two regimes: at moderate total solution concentrations where surface tension differences drive changes in extensional flow behavior, and at small PS:OVA ratios, which impacts the onset of weakly elastic behavior. Undesirable elasticity is observed in ultra-concentrated formulations, independent of PS identity; higher PS contents are required to observe these effects than with analogous polymeric excipient solutions. These studies reveal novel extensional flow behaviors in protein-excipient solutions, and provide a straightforward methodology for assessing the extensional flow stability of new protein-excipient formulations.

In this study, the dissociation constants of the eight amines, namely, N-(2-aminoethyl)-1,3-propanediamine, 2-methylpentamethylene diamine, N, n-dimethyldipropylene-triamine, 3,3’-Diamino-n-methyldipropylamine, Bis[2-(n, n-dimethylamino) ethyl]ether, 2-[2-(Dimethylamino) ethoxy] Ethanol, 2-(dibutylamino) Ethanol and N-propylethanolamine were determined from 298.15 K to 313.15 K. Using the van’t Hoff equation, thermodynamic properties such as the standard state changes of enthalpy, entropy and Gibbs free energy were calculated. Using computational chemistry calculations, the amino group protonated first was predicted. Furthermore, computer free group contribution methods such as the original Perrin-Dempsey-Serjeant (PDS), the modified PDS and the Qian-Sun-Sun-Gao (QSSG) model were used to estimate the dissociation constants of the studied amines. In these methods, the QSSG provided the most accurate results as the database used in this method was updated with additional the functional groups as well as information about group positions. Finally, an artificial neural network was used to predict the pKa values.

The utilization of lignin remains a great challenge due to its complex non-repetitive structure and the lack of efficient catalyst. Herein, a single-atom catalyst Ni@N-C was designed via a facile chelation-anchored strategy. Ni atoms were immobilized on the N-doped carbon carrier by a two-stage pyrolysis of a mixture of D-glucosamine hydrochloride, nickel acetate and melamine. D-glucosamine hydrochloride as a chelating agent prevented the aggregation of Ni2+, and melamine provided enough N to anchor Ni by forming Ni-N4 structure. Ni@N-C gave a 31.2% yield of aromatic compounds from lignin hydrogenolysis, which was twice higher than that achieved by Ni cluster catalyst. Based on the experimental and DFT calculation results, the higher activity of Ni@N-C was attributed to its lower H2 dissociation energy and the reduced energy barriers of the transition states. The strategy described opens an efficient green avenue for preparing single-atom catalyst that possesses outstanding activity in lignin depolymerization.

Modern chemical processes need to be operated around different operating conditions to optimize plant economy, in response to dynamic supply chains. As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference-flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade-off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.

This work considers a seeded fesoterodine fumarate (FF) cooling crystallization and presents the methodology and implementation of a real-time machine learning modeling-based predictive controller to handle batch-to-batch (B2B) parametric drift. Specifically, an autoencoder recurrent neural network-based model predictive controller (AERNN-MPC) is developed to optimize product yield, crystal size, and energy consumption while accounting for the physical constraints on cooling jacket temperature. Deviations in the kinetic parameters are considered in the closed-loop simulations to account for the B2B parametric drift, and two error-triggered online update mechanisms are proposed to address issues pertaining to the availability of real-time crystal property measurements and are incorporated into the AERNN-MPC to improve the model prediction accuracy. Closed-loop simulation results demonstrate that the proposed AERNN-MPC with online update, irrespective of the accessibility to real-time crystal property data, achieves a desired closed-loop performance in terms of maximizing product yield and minimizing energy consumption.

To validate the experimental results of Part-1, we conducted a two-phase flow simulation of imbibition of a wetting liquid through 2D microstructures made of ellipses of varying aspect ratios. The flow simulation in the particulate microstructures, characterized by low (ellipse) aspect ratio, produced somewhat even micro-fronts, thus replicating the sharp fronts at the visual (macroscopic) scale observed in Part-1. Whereas simulations in the fibrous microstructures produced highly uneven micro-fronts, suggesting the formation of semi-sharp or diffuse visual fronts. Increasing the porosity from 50% to 70% resulted in solid-phase clustering and led to further increase in the unevenness of micro-fronts, pointing to purely diffuse visual fronts. The evolution of the saturation plots along the flow direction, obtained from area-averaging of fluid-distribution plots, pointed to diffusing of sharp fronts with time. The predictions matched our previous experimental observations, i.e., the particulate media create sharp fronts while the fibrous media create semi-sharp/diffuse fronts.

The dynamics and breakup of bubbles in swirl-venturi bubble generator (SVBG) are explored in this work. The three-dimensional movement process and breakup phenomena of bubbles are captured by one high-speed camera system with two cameras while the distribution of swirling flow field are recorded through Particle Image Velocimetry technology. It is revealed that bubbles have two motion trajectories, which are deeply related to bubble breakup. One trajectory is that mother bubble moves upward in an axial direction of the SVBG to the diverging section, and the other trajectory is that mother bubble rotates obliquely upward to another side-wall along the radial direction. Meanwhile, binary breakup, shear-off-induced breakup, static erosive breakup and dynamic erosive breakup are observed. For relatively high liquid Reynolds number, vortex flow regions are extended and the bubble size is reduced. Furthermore, it is worth noting that the number of microbubbles increases significantly for intensive swirling flow.

The strong core base in chemical engineering during the latter half of the 20th century enabled chemical engineers to contribute extensively to many areas outside of the traditional. The depth of such involvement has led researchers to confront questions much more engaging to the field of application, thus adopting and cultivating expertise more native to it than to secure chemical engineering as a discipline. The progress of knowledge in science and engineering must leave a strong trail of fundamental understanding through developed methodologies that can assist in continuing progress. If this tenet is acknowledged, this article yields considerable scope for discussion on whether chemical engineering research is continuing to provide for a growing core that has endowed chemical engineers with the ability to formulate and solve important societal problems in which material systems undergo changes in composition and energy. We discuss opportunities for hopefully serving the issues of concern.

The state estimation and sensor placement for a continuous pulp digester with delayed measurements are investigated. The underlying model of interest is heat transfer in a pulp digester modeled by two coupled hyperbolic partial differential equations and an ordinary differential equation. Output measurements are considered with delay due to the possible low sampling rate. The Cayley-Tustin transformation is utilized to realize model time discretization in a late lumping manner which does not account for any type of spatial approximation or model reduction. The discrete Kalman filter is applied to estimate the system states using the delayed measurements. The selection of sensor location is addressed along with estimator design accounting for the delayed measurements and investigated by minimizing the variance of estimation error. The performance of the state estimator is evaluated, and the sensor placement is analyzed through simulation studies, which provide guidance for sensor location selection in industrial applications.

Single-atom catalysts with optimal atom utilization and outstanding activity have penetrated the frontier of heterogeneous catalysis. However, the large-scale synthesis of this class of catalysts is still a bottleneck for their industrialization. Herein, we suggest a two-stage micro-dispersion approach to synthesize mesoporous MgAl2O4-supported atomically dispersed Rh, which is more competitive than the batch method for boosting the uniform dispersion of Rh. By increasing the Rh loading, single-atom catalysts (SACs, < 0.05wt%), single-atom catalysts + nanoparticle catalysts (0.05–0.17 wt%), and nanoparticle catalyst (NPCs, 0.17–1.10 wt%) were obtained. For n-octane steam reforming, the turnover frequency of the SAC (0.01 wt%) was approximately 30 times that of the NPC (1.10 wt%), while the Rh amount of the SAC was only 3% that of the NPC for the same fuel conversion. Under a high-temperature (750 ℃) steam atmosphere for 15 h, the hydrogen formation rate only declined from 25.1 to 23.8 mol/mol-C8H18.