Explore the vast range of titles available.

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. It is of high interests to design catalysts for CO2 electroreduction with enhanced selectivity and activity. Here, the authors report Sn-Bi bimetallic interface-rich material with enhanced performance for CO2 reduction to formate comparing to that of Sn-Bi bulk alloy.

Periodic rescheduling is a commonly used method for scheduling short-term operations. Through computational experiments that vary plant parameters, such as the load and the capacity of a facility, we investigate the effects these parameters have on plant performance under periodic rescheduling. The results show that choosing a suitable rescheduling policy depends highly on some key plant parameters. In particular, by modifying various parameters of the facility, the performance ranking of the various rescheduling policies may be reversed compared to the results obtained with nominal parameter values. This highlights the need to consider both facility characteristics and what the crucial objective of the facility is when selecting a rescheduling policy. This study considers a variant of the job shop problem, used to model the operation of an industrial-scale analytical services facility using different periodic rescheduling policies. A rolling horizon routine is used to schedule operations over the scheduling horizon. Performance is measured in terms of job throughput, job makespan, and proportion of jobs on time at the end of the scheduling horizon to obtain a more complete understanding of how performance varies between rescheduling policies.

Uncertainty modelling is key to obtain a realistically feasible solution for large-scale optimization problems. In this study, we consider two-stage stochastic programming to model discrete-time batch process operations with a type II endogenous (decision dependent) uncertainty, where time of uncertainty realizations are dependent on the model decisions. We propose an integer programming model to solve the problem, whose key feature is that it does not require auxiliary binary variables or explicit non-anticipativity constraints to ensure non-anticipativity. To the best of our knowledge this is the first model dealing with such type II uncertainties that has these characteristics, which makes it a much more computationally attractive model. We present a proof that non-anticipativity is enforced implicitly as well as computational results using a large-scale scientific services industrial plant. The computational results from the case study depicts significant benefits in using the proposed stochastic programming approach.

The aim of this study is to present a Power Series Expansion (PSE)-based back-off methodology to consider probabilistic-based descriptions for the disturbances and uncertain parameters for integration of design and control. The key idea is to represent the expected value and standard deviation of the cost and process constraints using PSE-based functions. Thus, the present back-off approach identifies process designs that are dynamically operable at specific probability (confidence) levels defined a priori. Monte Carlo sampling is employed to design the PSE-based functions. A waste water treatment plant is used to test the performance of the proposed back-off approach.

Copper losses during the Peirce-Smith converter (PSC) operation is of great concern in the copper smelting process. Two primary objectives of the PSC are to produce blister copper with a shorter batch time and to keep the copper losses at a minimum level. Due to the nature of the process, those two objectives are contradictory to each other. Moreover, actions inside the PSC are subject to several operational constraints that make it difficult to develop a scheduling framework for its optimal operation. In this work, a basic but efficient linear multi-period scheduling framework for the PSC is presented that finds the optimal timings of the PSC operations to keep the copper losses and the batch time at a minimum level. An industrial case study is used to illustrate the effectiveness of the proposed framework. This novel solution can be implemented in other smelting processes and used for the design of an inter-PSC scheduling framework.

CO electroreduction reaction offers an attractive approach to global carbon neutrality. Industrial CO 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 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 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 electroreduction.

Chitosan is a high-value compound in the world market and can be obtained, mostly, from crustaceans, as they are shrimps, crabs, and lobsters, but other sources are fungal cell walls and algae. In 2027, the size of the market is estimated at 28.93 billion dollars according to intelligence on emerging markets for academic institutions around the world, (EMIS ®). Chitosan, is composed of β-(1→ 4) D-glucosamine and N-acetyl-D-glucosamine units and typically is the result of the deacetylation of chitin. Technically, this compound is a biopolymer and represents 30 to 40 % of the structure of the exoskeleton of the shrimp. Through qualitative and quantitative methodologies, the properties of chitosan depends on the deacetylation degrees and molecular weight. Currently, of the total chitosan produced worldwide, 23 % is destined for the pharmaceutical 22 % for the food industry, 18 % for the cosmetic industry, and finally water treatment with 17 %. In the pharmaceutical industry, the highest degrees of deacetylate were used, between 70 to 90 %. This work exposes the level of investigation using 3 search equations, using Scopus® as the main database and Vosviewer® to understand the relationship between keywords. Additionally, the main countries that were published, main authors, and areas of interest, among other topics are analyzed. Asian countries such as China or Japan are the largest researchers. This is the result of the fact that they are the countries with the highest investment the areas of interest will focus on the chemical engineering environment and chemical engineering. The pharmaceutical, food, and tissue engineering industries are the means of greatest information (75 % of the total researcher).

A framework for integration of design, control, and scheduling of multi-product plants under uncertainty and/or disturbances is presented. The framework uses a decomposition algorithm consisting of a flexibility optimization problem and a feasibility analysis, where critical realizations in disturbance and uncertainty are identified. Grade transitions are explicitly accounted for in the proposed framework, using flexible finite elements in the time discretization. When applied to a plug flow reactor (PFR), this framework is shown to provide robust solutions that are superior to simple back-off methods of finding robust solutions.

As part of the first anniversary issue of Nature Chemical Engineering , we present a collection of opinions from 40 researchers within the field on what they think are the most exciting opportunities that lie ahead for their respective topics.

This research work presents a new methodology for the simultaneous design and control of chemical processes. One of the most computationally demanding tasks in the integration of process control and process design is the search for worst case scenarios that result in maximal output variability or in process variables being at their constraint limits. The key idea in the current work is to find these worst scenarios by using tools borrowed from robust control theory. To apply these tools, the closed-loop dynamic behaviour of the process to be designed is represented as a robust model. Accordingly, the process is mathematically described by a nominal linear model with uncertain model parameters that vary within identified ranges of values. These robust models, obtained from closed-loop identification, are used in the present method to test the robust stability of the process and to estimate bounds on the worst deviations in process variables in response to external disturbances. The first approach proposed to integrate process design and process control made use of robust tools that are based on the Quadratic Lyapunov Function (QLF). These tests require the identification of an uncertain state space model that is used to evaluate the process asymptotic stability and to estimate a bound (γ) on the random-mean squares (RMS) gain of the model output variability. This last bound is used to assess the worst-case process variability and to evaluate bounds on the deviations in process variables that are to be kept within constraints. Then, these robustness tests are embedded within an optimization problem that seeks for the optimal design and controller tuning parameters that minimize a user-specified cost function. While the γ-based robust performance criterion provides a random-mean squares measure of the variability, it does not provide information on the worst possible deviation. In order to search for the worst deviation, the present work proposed a new robust variability measure based on the Structured Singular Value (SSV) analysis, also known as the μ-analysis. The results show that this new robust variability tool is computationally efficient and it can be potentially implemented to achieve the simultaneous design and control of chemical plants. Finally, the Structured Singular Value-based (μ-based) methodology was used to perform the simultaneous design and control of the Tennessee Eastman (TE) process. To study the interactions between design and control in the reactor's section of the plant, the effect of different parameters on the resulting design and control schemes were analyzed. Comparisons between the analytical bound based strategy and the simulation based strategy were discussed. Additionally, a comparison of the computational effort required by the present solution strategy and that required by a Dynamic Programming based approach was conducted. The results obtained from this research project show that Dynamic Programming requires a CPU time that is almost two orders of magnitude larger than that required by the methodology proposed here. Likewise, the consideration of uncertainty in a physical parameter within the analysis, such as the reaction rate constant in the Tennessee Eastman problem, was shown to dramatically increase the computational load when compared to the case in which there is no process parametric uncertainty in the analysis. (Abstract shortened by UMI.)