Hasil untuk "Chemical engineering"

A. Gupta, Mona Gupta

J. Gmehling, M. Kleiber, B. Kolbe et al.

With the ever increasing application of process simulation software tools like Aspen Plus®, ProSimPlus®, Hysys®, CHEMCAD®, Pro/II®, UNISIM® etc., engineers are confronted with the vast complexity of the underlying models and thermodynamic relationships. A sound knowledge and intuitive understanding of these process engineering fundamentals is vital for the development (synthesis), design and optimization of chemical processes. It is generally accepted, that any flaw in the underlying models and parameters usually leads to unrealistic simulation results. Within this very popular course (approx. 1000 participants in the last 15 years) professionals from industry and academics will become familiar with the possibilities and limitations of currently used methods and models. The course focuses on those aspects, which I consider to be of primary importance for the successful modeling of single separation units or whole chemical plants. Besides the thermodynamic properties of pure components, especially the behavior of multicomponent mixtures will be covered with special attention to phase equilibria, also those of electrolyte systems. The presentation is organized in four parts: • Basic pure component and mixture behaviors are presented together with the models that are typically employed in process simulation (equations of state, gE-models, and special correlations for pure component properties like e.g. vapor pressure). This includes discussion of VLE (separation factor, azeotropic behavior, ...) and miscibility gaps, gas solubility, solid solubility, ... and covers the different ways to obtain especially the binary interaction parameters (BIP). • Estimation methods for pure component properties (mainly group contribution) and mixture behavior (UNIFAC, mod. UNIFAC, PSRK; ...) are vital in cases no experimental data are available. Their basis and range of applicability will be discussed in detail. • Following the basics of thermodynamics, models and property estimation, various approaches to process engineering problems using modern thermodynamic methods will be presented. These include for example hybrid or pressure swing processes, the selection of suitable entrainers for special separation processes like azeotropic and extractive distillation and extraction. In this part, participants should gain an improved understanding of the various graphical representations of the real behavior of mixtures such as plots on solvent-free basis, contour lines, residual curves incl. boundary lines or surfaces, azeotropic points ...). • Following the first 3 days an optional fourth day offers a workshop on thermophysical properties in the Aspen Plus® simulator by Dr. Christian Möllmann and a parallel training day using the CHEMCAD simulator by Juergen Rarey. Practical tutorials are included to deepen the understanding of the various topics. The course will be held in the English language.

Lei Zhang

The quantum threat to cybersecurity has accelerated the standardization of Post-Quantum Cryptography (PQC). Migrating legacy software to these quantum-safe algorithms is not a simple library swap, but a new software engineering challenge: existing vulnerability detection, refactoring, and testing tools are not designed for PQC's probabilistic behavior, side-channel sensitivity, and complex performance trade-offs. To address these challenges, this paper outlines a vision for a new class of tools and introduces the Automated Quantum-safe Adaptation (AQuA) framework, with a three-pillar agenda for PQC-aware detection, semantic refactoring, and hybrid verification, thereby motivating Quantum-Safe Software Engineering (QSSE) as a distinct research direction.

Alexander Korn, Lea Zaruchas, Chetan Arora et al.

Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a critical factor in system performance and behavior. Despite their growing role in SE research, prompt-related decisions are rarely documented in a systematic or transparent manner, hindering reproducibility and comparability across studies. To address this gap, we conducted a two-phase empirical study. First, we analyzed nearly 300 papers published at the top-3 SE conferences since 2022 to assess how prompt design, testing, and optimization are currently reported. Second, we surveyed 105 program committee members from these conferences to capture their expectations for prompt reporting in LLM-driven research. Based on the findings, we derived a structured guideline that distinguishes essential, desirable, and exceptional reporting elements. Our results reveal significant misalignment between current practices and reviewer expectations, particularly regarding version disclosure, prompt justification, and threats to validity. We present our guideline as a step toward improving transparency, reproducibility, and methodological rigor in LLM-based SE research.

Yun Zhang, Yiwen Guo, Kaibo Sun et al.

Zn-doped carbon dots (Zn@C-210 calcination temperature at 210 °C and Zn@C-260 calcination temperature at 260 °C) were synthesized via an in situ calcination method using zinc citrate complexes as precursors, aiming to investigate the mechanisms of their distinctive fluorescence properties. A range of analytical methods were employed to characterize these nanomaterials. The mechanism study revealed that the coordination structure of Zn-O, formed through zinc doping, can induce a metal–ligand charge-transfer effect, which significantly increases the probability of radiative transitions between the excited and ground states, thereby enhancing the fluorescence intensity. The Zn@C-210 in a solid state and Zn@C-260 in water exhibited approximately 71.50% and 21.1% quantum yields, respectively. Both Zn@C-210 and Zn@C-260 exhibited excitation-independent luminescence, featuring a long fluorescence lifetime of 6.5 μs for Zn@C-210 and 6.2 μs for Zn@C-260. Impressively, zinc-doped CDs displayed exceptional biosafety, showing no acute toxicity even at 1000 mg/kg doses. Zn@C-210 has excellent fluorescence in a solid state, showing promise in anti-photobleaching applications; meanwhile, the dual functionality of Zn@C-260 makes it useful as a folate sensor and cellular imaging probe. These findings not only advance the fundamental understanding of metal-doped carbon dot photophysics but also provide practical guidelines for developing targeted biomedical nanomaterials through rational surface engineering and doping strategies.

Haixu Chen, Zhengbin Han, Shengliang Wang et al.

Abstract Liquid-liquid phase separation plays an important role in many natural and technological processes. Herein, we implement lateral microphase separation at the surface of oil micro-droplets suspended in water to prepare a range of discrete floating protein/polymer continuous two-dimensional (2D) heterostructures with variable interfacial domain structures and dynamics. We show that gel-like domains of bovine serum albumin (BSA) co-exist with fluid-like polyvinyl alcohol (PVA) regions at the oil droplet surface to produce floating heterostructures comprising a 2D phase-separated protein mesh or an array of discrete mobile protein rafts depending on the conditions employed. Enzymes are embedded in the discontinuous BSA domains to produce droplet-supported microphase-separated 2D reaction scaffolds that can be tuned for interfacial catalysis. Taken together, our work has general implications for the structural and functional augmentation of oil droplet interfaces and contributes to the surface engineering and functionality of droplet-based micro-reactors.

赵巍, 韩雅倩, 张华 et al.

In order to investigate the critical snow formation height of the mixed single-aperture nucleator within the artificial snow machine, an industrial microscope was used to observe the microstructure of the snow crystals, measure the critical snow formation height threshold, and analyse the effect of the air-to-water pressure ratio (0.4MPa:0.4MPa, 0.5MPa:0.45MPa, and 0.5MPa:0.4MPa) and the ambient temperatures (-5℃, -10℃, and -15℃) on the critical snow formation height. The results showed that under the working condition of air-water pressure ratio of 0.4MPa:0.4MPa, when the temperatures were -5℃ and -10℃, the threshold of critical snow formation height did not exist, and when the temperature was -15℃, it was able to form snow, and the threshold of critical snow formation height was 50~55cm; when the air-water pressure ratios were 0.5MPa:0.45MPa, 0.5MPa:0.4MPa, the three ambient temperatures can form snow. And the gas-water pressure ratio and ambient temperature will have a certain effect on the critical snow height, under the same ambient temperature, the larger the gas-water pressure ratio, the smaller the critical snow height; The critical snow formation height increases as the ambient temperature increases from -15°C to -5°C while keeping the air-water pressure ratio constant. When the gas-water pressure ratio is 0.5MPa:0.45MPa, the trend of critical snow formation height with temperature is larger, and the temperature has a greater impact on the critical snow formation height. The results of the study can provide a basis for the design of the optimized arrangement between the nucleator and the nozzle of the snow-making machine.

Shavindra Wickramathilaka, John Grundy, Kashumi Madampe et al.

The use of diverse mobile applications among senior users is becoming increasingly widespread. However, many of these apps contain accessibility problems that result in negative user experiences for seniors. A key reason is that software practitioners often lack the time or resources to address the broad spectrum of age-related accessibility and personalisation needs. As current developer tools and practices encourage one-size-fits-all interfaces with limited potential to address the diversity of senior needs, there is a growing demand for approaches that support the systematic creation of adaptive, accessible app experiences. To this end, we present AdaptForge, a novel model-driven engineering (MDE) approach that enables advanced design-time adaptations of mobile application interfaces and behaviours tailored to the accessibility needs of senior users. AdaptForge uses two domain-specific languages (DSLs) to address age-related accessibility needs. The first model defines users' context-of-use parameters, while the second defines conditional accessibility scenarios and corresponding UI adaptation rules. These rules are interpreted by an MDE workflow to transform an app's original source code into personalised instances. We also report evaluations with professional software developers and senior end-users, demonstrating the feasibility and practical utility of AdaptForge.

Qiaolin Qin, Ronnie de Souza Santos, Rodrigo Spinola

Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.

Mauro Marcelino, Marcos Alves, Bianca Trinkenreich et al.

[Context] An evidence briefing is a concise and objective transfer medium that can present the main findings of a study to software engineers in the industry. Although practitioners and researchers have deemed Evidence Briefings useful, their production requires manual labor, which may be a significant challenge to their broad adoption. [Goal] The goal of this registered report is to describe an experimental protocol for evaluating LLM-generated evidence briefings for secondary studies in terms of content fidelity, ease of understanding, and usefulness, as perceived by researchers and practitioners, compared to human-made briefings. [Method] We developed an RAG-based LLM tool to generate evidence briefings. We used the tool to automatically generate two evidence briefings that had been manually generated in previous research efforts. We designed a controlled experiment to evaluate how the LLM-generated briefings compare to the human-made ones regarding perceived content fidelity, ease of understanding, and usefulness. [Results] To be reported after the experimental trials. [Conclusion] Depending on the experiment results.

Sophia Rupprecht, Qinghe Gao, Tanuj Karia et al.

Large language model (LLM)-based multi-agent systems (MASs) are a recent but rapidly evolving technology with the potential to transform chemical engineering by decomposing complex workflows into teams of collaborative agents with specialized knowledge and tools. This review surveys the state-of-the-art of MAS within chemical engineering. While early studies demonstrate promising results, scientific challenges remain, including the design of tailored architectures, integration of heterogeneous data modalities, development of foundation models with domain-specific modalities, and strategies for ensuring transparency, safety, and environmental impact. As a young but fast-moving field, MASs offer exciting opportunities to rethink chemical engineering workflows.

Max Ofsa, Taylan G. Topcu

Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-purpose Large Language Models (LLM) in supporting ME problem formulation tasks, specifically focusing on stakeholder identification. We identify a relevant reference problem, a NASA space mission design challenge, and document ChatGPT-3.5's ability to perform stakeholder identification tasks. We execute multiple parallel attempts and qualitatively evaluate LLM outputs, focusing on both their quality and variability. Our findings portray a nuanced picture. We find that the LLM performs well in identifying human-focused stakeholders but poorly in recognizing external systems and environmental factors, despite explicit efforts to account for these. Additionally, LLMs struggle with preserving the desired level of abstraction and exhibit a tendency to produce solution specific outputs that are inappropriate for problem formulation. More importantly, we document great variability among parallel threads, highlighting that LLM outputs should be used with caution, ideally by adopting a stochastic view of their abilities. Overall, our findings suggest that, while ChatGPT could reduce some expert workload, its lack of consistency and domain understanding may limit its reliability for problem formulation tasks.

Diana Robinson, Christian Cabrera, Andrew D. Gordon et al.

What if end users could own the software development lifecycle from conception to deployment using only requirements expressed in language, images, video or audio? We explore this idea, building on the capabilities that generative Artificial Intelligence brings to software generation and maintenance techniques. How could designing software in this way better serve end users? What are the implications of this process for the future of end-user software engineering and the software development lifecycle? We discuss the research needed to bridge the gap between where we are today and these imagined systems of the future.

Marco Autili, Martina De Sanctis, Paola Inverardi et al.

As testified by new regulations like the European AI Act, worries about the human and societal impact of (autonomous) software technologies are becoming of public concern. Human, societal, and environmental values, alongside traditional software quality, are increasingly recognized as essential for sustainability and long-term well-being. Traditionally, systems are engineered taking into account business goals and technology drivers. Considering the growing awareness in the community, in this paper, we argue that engineering of systems should also consider human, societal, and environmental drivers. Then, we identify the macro and technological challenges by focusing on humans and their role while co-existing with digital systems. The first challenge considers humans in a proactive role when interacting with digital systems, i.e., taking initiative in making things happen instead of reacting to events. The second concerns humans having a reactive role in interacting with digital systems, i.e., humans interacting with digital systems as a reaction to events. The third challenge focuses on humans with a passive role, i.e., they experience, enjoy or even suffer the decisions and/or actions of digital systems. The fourth challenge concerns the duality of trust and trustworthiness, with humans playing any role. Building on the new human, societal, and environmental drivers and the macro and technological challenges, we identify a research roadmap of digital systems for humanity. The research roadmap is concretized in a number of research directions organized into four groups: development process, requirements engineering, software architecture and design, and verification and validation.

Colani T. Fakude, Refiloe P. Modise, Aderemi B. Haruna et al.

Drug abuse has proliferated at an unprecedented rate worldwide, posing significant public health challenges that directly impact society, criminality, and the economy. This review presents the application of nanomaterials for qualitative and quantitative electrocatalytic analysis of drugs of abuse, mostly opioids (such as heroin (HER), morphine (MOR), codeine (COD), fentanyl (FEN), and tramadol (TR)), and addictive stimulants (such as cocaine (COC) and methamphetamine (MAM)) via direct oxidation. Electroanalytical techniques have attracted attention for generating point-of-use sensors because of their low cost, portability, ease of use, and the possibility of miniaturization. Electroanalytical-based devices can assist first responders with tools to identify unknown powders and to treat victims of drug abuse. Based on the drug therapeutic and usage purposes, research advances in drug electroanalysis can be classified and discussed with special emphasis on the electrochemical reaction mechanism of the drug. Therefore, this review discusses sensor enhancement based on the electrocatalytic properties introduced by various strategies, such as surface nanostructuring, the use of conducting polymers, and anodization of electrode surfaces Finally, a critical outlook is presented with recommendations and prospects for future development.

Chaoyu WANG, Tingye QI, Guorui FENG et al.

As a renewable and clean source, biomass energy is one of the substitutes for traditional fossil energy. However, when biomass is burned as an industrial fuel, it produces a large amount of biomass ash with considerable pozzolanic activity. Currently, the activity of biomass ash is ignored in the utilization of biomass energy. Therefore, research on the regulation mechanism of calcination temperature on the pozzolanic activity of biomass ash will facilitate its efficient utilization. Therefore, we reviewed previous research and selected 500, 700, and 850 ℃ temperatures to calcinate willow leaves. The contents of SiO2, CaO, and other oxides in the willow leaf ash were determined through X-ray fluorescence spectrometer(XRF). The specific surface area of willow leaf ash was determined using a laser particle size analyzer. The mineral composition of willow leaf ash was characterized by X-ray diffraction (XRD), and the characterization of the chemical bonds of the minerals was supplemented by Fourier-transform infrared (FTIR) spectroscopy. The zeta potential of the willow leaf ash–Ca(OH)2 solution was determined through microelectrophoresis to evaluate the system’s stability. After determining the basic physical and chemical properties of willow leaf ash, the mechanical properties of willow leaf ash–cement-based materials were investigated by replacing 20% (mass fraction) cement with the ash, and the factors affecting performance were analyzed. The pozzolanic activity of willow leaf ash at 500, 700, and 850 ℃ was evaluated through the activity index. Rapid evaluation of pozzolanic activity was conducted by active ion extraction capability and inductively coupled plasma-optical emission spectrometer (ICP-OES) analyses. Scanning electron microscopy and XRD characterization methods were combined to analyze the effect of calcination temperature on the structure and composition of the ash and to elucidate the mechanism of the effect of calcination temperature on its pozzolanic activity. The results show that the SiO2 content in the ash was 20% to 30%, and the specific surface area increased with increasing temperature. However, the presence of xonotlite in willow leaf ash was detected through XRD at 850 ℃ Furthermore, the observed FTIR absorption peak at 1120.74 cm−1 corresponded to the stretching vibration of the Si–O–Si structure, which indicated that some amorphous SiO2 was crystallized. The absolute value of the zeta potential of the solution containing willow leaf ash at 500 ℃ and 700℃ was considerably higher than that at 850℃. After replacing a part of the cement with willow leaf ash, the willow leaf ash–cement-based material exhibited the highest compressive strength at 500 ℃ with an activity index of 0.79. The rate of conductivity variation of the willow leaf ash–Ca(OH)2 solution at 500 ℃ and 700 ℃ was higher than that at 850 ℃. The concentration of Si4+ precipitation decreased with the increase in calcination temperature, indicating that willow leaf ash had the highest pozzolanic activity at 500 ℃ followed by 700 ℃. Excessively high calcination temperatures lead to the crystallization of amorphous SiO2 and slagging in willow leaf ash, along with a decrease in the pozzolanic activity. This study provides theoretical support for the regulation of the pozzolanic activity of biomass ash and its applications.

Chuqi He, Yucheng Yang, Mi Zhang et al.

Using plant-based polysaccharide gels to produce hard capsules is a novel application of this technology in the medicinal field, which has garnered significant attention. However, the current manufacturing technology, particularly the drying process, limits its industrialization. The work herein employed an advanced measuring technique and a modified mathematical model to get more insight into the drying process of the capsule. Low field magnetic resonance imaging (LF-MRI) technique is adopted to reveal the distribution of moisture content in the capsule during drying. Furthermore, a modified mathematical model is developed by considering the dynamic variation of the effective moisture diffusivity (<i>D</i>_eff) according to Fick’s second law, which enables accurate prediction of the moisture content of the capsule with a prediction accuracy of ±15%. The predicted <i>D</i>_eff ranges from 3 × 10⁻¹⁰ to 7 × 10⁻¹⁰ m²·s⁻¹, which has an irregular variation with a time extension. Moreover, as temperature increases or relative humidity decreases, there is an increased acceleration of moisture diffusion. The work provides a fundamental understanding of the drying process of the plant-based polysaccharide gel, which is crucial for enhancing the industrial preparation of the HPMC-based hard capsules.

Rafiqul Gani, Lei Zhang, Chrysanthos Gounaris

LI Han, ZHANG Botao, WANG Junjie, SUN Yunda, GONG Shengjie

This paper uses a single fiber bragg grating (FBG) sensor to implement an experiment to measure vibration and temperature signals at the same time, and proposes a MATLAB-based decoupling method to separate vibration and temperature signals. The experimental results show that under the condition of single signal measurement, the static temperature measurement error of the FBG sensor is within ±0.4 ℃ and the relative error of the dynamic measurement of the main frequency of vibration is 0.5%. The FBG sensor measures the composite signal of vibration and temperature. The relative error of the main vibration frequency obtained by the decoupling method proposed in this experiment is 0.65%, the relative error of the vibration amplitude is 7.14%, and the temperature signal error is within ±3.3 ℃.

Suparna Samavedham, S. Lakshminarayanan

The ability to generate, organize, analyze, understand and leverage data for sound decision making is a central activity of chemical engineers. Chemical engineers are responsible for the safe, profitable and environmentally friendly operation of chemical facilities; thus, they are expected to be good at designing and operating chemical processes. To this end, they make use of models which involves planning and conducting experiments in the laboratory or a pilot plant, analyzing the generated data and making use of it for designing large scale industrial systems. In an operating plant, they need to analyze data so as to achieve maximum efficiencies, reduce the use of precious natural resources, minimize environmental degradation, keep the plant safe as well as to help generate value for the customers and stakeholders. This chapter provides a non-technical view of how chemical engineers translate data to the best possible decisions that result in reliable, safe and profitable process design and operations.