Current Opinion in Chemical Engineering最新文献

Traditionally, the pharmaceutical industry relied on resource-intensive and empirical methods for process development, optimization, and control. Heuristic approaches to pharmaceutical development and manufacturing have led to an unsustainable number of drug shortages and recalls and to escalating costs for launching new drug products. Optimization and control strategies rooted on process modeling are helping to advance pharmaceutical manufacturing by reducing development times and manufacturing costs, improving productivity and quality control, and enhancing process understanding. This perspective discusses recent developments toward model-based optimization, state estimation, and control of pharmaceutical processes. Ancillary areas such as software tools, equipment and sensor technology, and process modeling are first covered. Then, several recent academic and industrial case studies are discussed to highlight workflows and benefits related to the implementation of model-based optimization, state estimation, and control in (bio)pharmaceutical manufacturing. Finally, strategies for overcoming current challenges in the real-world application of model-based optimization and control are discussed.

Nitrosamine risk assessments are useful to identify processes and/or materials that exhibit legitimate safety concerns to evaluate appropriate actions and controls. Fundamental understanding of nitrosamine toxicity, formation pathways, fate, and purge aid risk quantification. In this work, available digital tools to aid nitrosamine risk assessments are reviewed. From structure–activity relationships through kinetic models to purge factors, these tools can shed light on the various aspects of a nitrosamine risk assessment. While tremendous progress has been made over the last few years, industry, academia, and government still need to focus on supporting science-based decisions and the integration of diverse tools to provide a holistic view of the nitrosamine risk.

Metal–organic frameworks (MOFs) have attracted much attention in photocatalysis due to their unique molecular architecture and diverse composition, where the performance can be effectively enhanced by the construction of heterojunctions. In this review, we summarize the recent progress of MOF-based photocatalysts with traditional heterojunctions (e.g. type I, type II, and p-n type) as well as Z- and S-scheme and even multicomponent heterojunctions, where the advantages and disadvantages of these heterojunctions in their photocatalytic applications toward environmental remediation and energy conversion are critically discussed. The review is concluded with a perspective for the further development of high-performance photocatalysts based on deliberate heterojunction engineering of MOF materials.

The primary objectives of pharmaceutical development encompass identifying the routes, processes, and conditions for producing medicines while establishing a control strategy to ensure acceptable quality attributes throughout the commercial manufacturing lifecycle. However, achieving these goals is challenged by uncertainties surrounding design decisions for the manufacturing process and variations in manufacturing methods resulting in distributions of outcomes during production. In this discussion, we focus on Bayesian approaches to quantify uncertainty and guide decision-making in process development.Bayesian modeling with Markov chain Monte Carlo proves effective in estimating process reliability. Recent advancements in surrogate models (e.g. Gaussian process, decision trees, and neural networks) offer novel means to quantify uncertainty and have shown success in designing experimental plans that reduce the number of required experiments to determine the optimal process design. By leveraging Bayesian approaches, chemical engineers can enhance their ability to navigate complex decision landscapes and optimize processes for improved efficiency and reliability.

Single-atom catalysts (SACs) have attracted widespread interest due to their exceptional properties in heterogeneous catalysis. Recently, Z-scheme photocatalysts-based SACs are emerging as a versatile catalytic system for environmental applications. This review succinctly introduces the synthetic strategies for SAC-based Z-scheme photocatalysts and highlights their recent progress in advanced oxidation processes (AOPs). The applications of Z-scheme SAC-mediated AOPs in wastewater treatment are summarized, including photo-Fenton-like reactions, photocatalysis, and piezo-photocatalysis technologies. Additionally, the advantages of the Z-scheme heterojunction in photocatalysis and the corresponding Z-scheme charge-transfer mechanisms are discussed. This review offers valuable insights into the potential applications of SAC-based Z-scheme photocatalysts for environmental remediation and aims to inspire future research on advanced SACs.

The aim of this paper is to assess carbon dioxide (CO₂) reduction research and strategies, in view of the 2030 Sustainable Development Goals (SDG), and in particular SDG 13 ‘Climate Action’, achievement. According to current understanding, limiting global warming to a determined temperature level requires stabilization of atmospheric greenhouse gases by achieving net-zero emissions. This implies that all residual emissions should be counterbalanced by natural or technical long-term storage sinks. Therefore, CO₂ reduction and storage appear to be essential for curtailing global warming and for the stabilization of global climate, as a precondition for the achievement of SDGs. Catalyst preparation has a high impact on carbon removal technology, especially as far as the most promising methods, such as electrochemical and photochemical reduction, are concerned. Even if the two technologies might preliminarily appear different, the inner mechanisms, that is, electron transfer and surface absorption, are common to both. This study analyses recent literature on CO₂ catalytic reduction through the analysis of network maps created by VOSviewer to spotlight the most promising areas for the sector’s improvement.

Since its creation and diversification 30 years ago, reversible deactivation radical polymerization (RDRP) has gained tremendous relevance as a powerful and versatile set of techniques for the robust synthesis of precise polymer architectures for advanced applications. In parallel, mathematical models for the polymerization kinetics, molar mass distributions, and copolymer characteristics have rapidly developed and gained sophistication and detail. The aim of this review is to provide a summary of the most important modeling techniques used in RDRP and a brief description of the most recent literature on the subject, highlighting the most relevant issues and subjects addressed in these works.

Clinical advances in the space of next-generation vaccines and medicines, together with the advent of platform technologies, have revolutionised the modus operandi of the biopharmaceutical industry. The industry’s core mission for uninterrupted delivery of high-quality, efficacious drugs is now further challenged by bold and necessary targets to operate sustainably. In this paper, we discuss how threefold sustainability that integrates economic, environmental, and social objectives can be achieved through a combination of technological advances, expert knowledge and transparent dialogue. We summarise recent advances in biomanufacturing in terms of novel technologies, intracellular and process-level analytics, and computational models before providing an outlook on how Industry 5.0 principles can be adopted and applied to advance industrial practice.

Because of increasing demand and strict regulations, pharmaceutical manufacturers encounter significant hurdles in achieving high productivity while ensuring normal process states. Variability in raw materials and operational disturbances can lead to deviations from normal operating conditions that result in decreased productivity. The implementation of smart fault detection and diagnosis (FDD) techniques is crucial for attaining acceptable productivity and ensuring process safety. In this review, we identify the major challenges of smart FDD in pharmaceutical processes, and we discuss future opportunities and new perspectives.

This review meticulously explores the innovative application of iron-based catalysts in addressing the pressing environmental challenge of removing nitrogen and per- and polyfluoroalkyl substances (PFAS) from wastewater. Traditional wastewater treatment methods fall short of efficiently eliminating these pervasive pollutants, necessitating the development of more effective, sustainable, and economically viable alternatives. Iron-based catalysts, characterized by their exceptional catalytic reduction capabilities, offer a promising solution. Through various forms, including zero-valent iron, iron oxides, and bimetallic combinations, these catalysts demonstrate a unique ability to break down nitrogen oxides and PFAS into less harmful substances. The review underscores the catalysts’ advantages in terms of reactivity, cost-efficiency, and environmental impact, highlighting their potential to transform wastewater treatment practices. However, it also acknowledges existing challenges, such as catalyst stability and the need for further optimization, to facilitate widespread application. This work advocates for intensified research efforts to refine these catalytic systems, aiming to integrate them seamlessly into future wastewater treatment infrastructures, thereby contributing to the preservation of water quality and public health.