Abstract
CO
2
electroreduction reaction offers an attractive approach to global carbon neutrality. Industrial CO
2
electrolysis towards formate requires stepped-up current densities, which is limited ...by the difficulty of precisely reconciling the competing intermediates (COOH* and HCOO*). Herein, nano-crumples induced Sn-Bi bimetallic interface-rich materials are in situ designed by tailored electrodeposition under CO
2
electrolysis conditions, significantly expediting formate production. Compared with Sn-Bi bulk alloy and pure Sn, this Sn-Bi interface pattern delivers optimum upshift of Sn p-band center, accordingly the moderate valence electron depletion, which leads to weakened Sn-C hybridization of competing COOH* and suitable Sn-O hybridization of HCOO*. Superior partial current density up to 140 mA/cm
2
for formate is achieved. High Faradaic efficiency (>90%) is maintained at a wide potential window with a durability of 160 h. In this work, we elevate the interface design of highly active and stable materials for efficient CO
2
electroreduction.
•Current challenges for integration of design and control are identified.•Perspectives for simultaneous design and control are provided.•Outlooks from process design to supply chain management are ...introduced.•New frontiers for decision-making strategies of chemical supply chain are presented.
Enterprise-wide sustainability involves aspects from molecular-scale design to macroscale facilities. Typically, these aspects are treated as independent sub-problems. Maintaining consistent information flow among the sub-problems in an integrated fashion provides additional opportunities in pursuing the ultimate goals of sustainable environmentally-friendly processes. Integration of design and control represents the core and foremost component of such an integrated approach. In this study, the major challenges of the core problem are classified. Perspectives on the advances, new frontiers, and emerging areas in this field are also addressed. Emerging trends such as advanced techniques to connect the sub-problems in the chemical supply chain, incorporation of expert decisions and sophisticated modeling techniques will be discussed. Much more work is still needed to determine a comprehensive vision of the integrated problem. Such achievement requires efficient programming techniques, mature data analysis procedures, and high-performance computing environments.
•A novel hybrid multiscale model is developed to simulate thin film deposition.•Continuum models and stochastic PDE used to capture deposition multiscale behaviour.•Artificial neural networks used to ...predict SPDE's model parameters.•Hybrid multiscale model validated using kinetic Monte Carlo based thin film model.•Hybrid multiscale model used to perform optimization & control on thin film growth.
This work details the construction and evaluation of a low computational cost hybrid multiscale thin film deposition model that couples artificial neural networks (ANNs) with a mechanistic (first-principles) multiscale model. The multiscale model combines continuum differential equations, which describe the transport of the precursor gas phase, with a stochastic partial differential equation (SPDE) that predicts the evolution of the thin film surface. In order to allow the SPDE to accurately predict the thin film growth over a range of system parameters, an ANN is developed and trained to predict the values of the SPDE coefficients. The fully-assembled hybrid multiscale model is validated through comparison against a kinetic Monte Carlo-based thin film multiscale model. The model is subsequently applied to a series of optimization and control studies to test its performance under different scenarios. These studies illustrate the computational efficiency of the proposed hybrid multiscale model for optimization and control applications.
•Artificial Neural Networks (ANNs) were trained on stochastic multiscale model data.•ANNs were used in online nonlinear model predictive control scheme.•ANNs provided accurate predictions for ...industrially relevant observable values.•ANN computational costs were orders of magnitude lower than the original model.•The accuracy of ANNs deteriorated for observable values subject to stochastic noise.
The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-driven models that would enable the shrinking horizon nonlinear model predictive control of a computationally intensive stochastic multiscale system. The system of choice was a simulation of thin film formation by chemical vapour deposition. Two ANNs were trained to estimate the system’s observables. The ANNs were subsequently employed in a shrinking horizon optimization scheme to obtain the optimal time-varying profiles of the manipulated variables that would meet the desired thin film properties at the end of the batch. The resulting profiles were validated using the stochastic multiscale system and a good agreement with the predictions of the ANNs was observed. Due to their observed computational efficiency, accuracy, and the ability to reject disturbances, the ANNs seem to be a promising approach for online optimization and control of computationally demanding multiscale process systems.
A trust‐region approach is presented to address the simultaneous design and control of large‐scale systems under uncertainty. The key idea is to represent the system using power series expansions ...(PSEs) as piecewise models in an iterative manner while the validity of those expansions is certified in a trusted region. The mean of squared errors is used as a metric to quantify the accuracy of the PSE approximations. Identified search regions specify the boundaries of the decision variables for the PSE‐based optimization problems. The proposed algorithm shows a significant accomplishment in locating dynamically feasible and near‐optimal design and operating conditions. The proposed approach was tested in a wastewater treatment plant and the Tennessee Eastman process. The results indicate that the proposed methodology leads to more economically attractive and reliable designs while maintaining the dynamic operability of the system in the presence of disturbances and uncertainty.
Industrial production of valuable chemical products often involves the manipulation of phenomena evolving at different temporal and spatial scales. Product properties can be captured accurately using ...computationally expensive stochastic multiscale models that explicitly consider the feedbacks between different scales. However, product design quality is often tampered by uncertainties affecting process operation. In this work, we used artificial neural networks (ANNs) to estimate an uncertain parameter, accurately predict product properties under uncertainty, and achieve orders-of-magnitude computational savings of a multiscale model of thin film formation by chemical vapor deposition. ANNs were trained using multiple realizations of the uncertain parameter to capture the behavior of the thin film’s two key microscale properties: roughness and growth rate. Next, mean square error and maximum likelihood estimation were used for parameter estimation and to find the ANN that could generate the closest predictions to the real-time measurements collected from the process in the presence of uncertainty. The chosen ANNs were employed to seek for the optimal operating conditions to enable the fabrication process to meet product quality specifications. ANNs are a promising technique for product property prediction and efficient decision making in the design of optimal operating conditions for chemical processes under uncertainty.
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•A phase diagram was established to find the most stable LSF structure.•The role of oxygen non-stoichiometry and Ni/Mn doping were analyzed.•Ni-Mn co-doping is proposed to improve the ...LSF performance for CO2 reduction.
To improve the catalytic activity of La(Sr)FeO3 based perovskites (LSF) for CO2 reduction in solid oxide electrolysis cell (SOEC), CO2 adsorption and reduction reaction mechanism were investigated on 12 surface models describing the effects of surface oxygen vacancies and Ni/Mn doping (25% and 50% surface cation doping ratios). In particular, a phase diagram was established to find the most stable LSF structure under SOEC operating conditions. These were carried out using Density Functional Theory (DFT) + U calculations. A microkinetic model was then developed to simulate polarization curves and compared with the experimental data of pure LSF. Ni-Mn double doping with 2 surface oxygen vacancies of LSF was identified as the most effective electrocatalysts. This is attributed to fine tuning O affinity by Ni, Mn and Fe in B-site (catalytic active site), as indicated by the Bader charge analysis. Experimental studies for this material have yet to be reported in the literature.
In this work, we investigate the performance of nonlinear model predictive control (NMPC) and moving horizon estimation (MHE) in a feedback control system subject to different arrival cost (AC) ...approximation methods, process uncertainties with non-Gaussian distributions, and plant designs. In particular, we investigate the performance of an extended Kalman filter (EKF) as an AC estimator for large and complex applications. Considering the significant impact of state estimations as the initial condition of the NMPC problem, together with the importance of the AC approximation in the success of the MHE framework, it is expected that a poor approximation of the AC may lead to poor closed-loop performance. Different arrival cost estimation methods including the traditional EKF and constrained particle filtering were evaluated in this work. The closed-loop framework was tested on two industrial applications: a wastewater treatment plant and a high-impact polystyrene process. Error analysis on the convergence of the EKF-based AC estimator is presented in this work to provide insights into the performance of EKF as an AC estimator under different scenarios. The results show that an appropriate arrival cost estimation method such as EKF is adequate to maintain the operation of large and challenging systems in a closed-loop using an MHE–NMPC framework.
Flexible Zn‐air batteries have recently emerged as one of the key energy storage systems of wearable/portable electronic devices, drawing enormous attention due to the high theoretical energy ...density, flat working voltage, low cost, and excellent safety. However, the majority of the previously reported flexible Zn‐air batteries encounter problems such as sluggish oxygen reaction kinetics, inferior long‐term durability, and poor flexibility induced by the rigid nature of the air cathode, all of which severely hinder their practical applications. Herein, a defect‐enriched nitrogen doped–graphene quantum dots (N‐GQDs) engineered 3D NiCo2S4 nanoarray is developed by a facile chemical sulfuration and subsequent electrophoretic deposition process. The as‐fabricated N‐GQDs/NiCo2S4 nanoarray grown on carbon cloth as a flexible air cathode exhibits superior electrocatalytic activities toward both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), outstanding cycle stability (200 h at 20 mA cm−2), and excellent mechanical flexibility (without observable decay under various bending angles). These impressive enhancements in electrocatalytic performance are mainly attributed to bifunctional active sites within the N‐GQDs/NiCo2S4 catalyst and synergistic coupling effects between N‐GQDs and NiCo2S4. Density functional theory analysis further reveals that stronger OOH* dissociation adsorption at the interface between N‐GQDs and NiCo2S4 lowers the overpotential of both ORR and OER.
A highly efficient and flexible bifunctional air cathode is successfully constructed by defect‐enriched nitrogen doped–graphene quantum dots engineered 3D NiCo2S4 nanoarray, which exhibits excellent electrochemical activity and long‐term durability. Importantly, the obtained rechargeable and flexible Zn‐air battery shows high power density, outstanding cycle stability, and excellent mechanical flexibility, making it a promising candidate for powering portable/wearable devices.
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