Process Engineering Research Articles

Multi-Criteria Decision Making in Chemical and Process Engineering: Methods, Progress, and Potential

Multi-criteria decision making (MCDM) is necessary for choosing one from the available alternatives (or from the Pareto-optimal solutions obtained by multi-objective optimization), where the performance of each alternative is quantified against several criteria (or objectives). This paper presents a comprehensive review of the application of MCDM methods in chemical and process engineering. It systematically outlines the essential steps in MCDM including the various normalization, weighting, and MCDM methods that are critical to decision making. The review draws on published papers identified through a search in the Scopus database, focusing on works by authors with more contributions to the field and on highly cited papers. Each selected paper was analyzed based on the MCDM, normalization, and weighting methods used. Additionally, this paper introduces two readily available programs for performing MCDM calculations. In short, it provides insights into the MCDM steps and methods, highlights their applications in chemical and process engineering, and discusses the challenges and prospects in this area.

Compaction Evolution and Mechanisms of Granular Materials Due to Gyratory Shearing

Granular systems, no matter whether they are dry or saturated, are commonly encountered in both natural scenarios and engineering applications. In this work, we tackle the compaction problem of both dry and saturated granular systems under gyratory shearing compaction, where particles are subjected to constant pressure and continuous shear rate, which is quite different from the traditional cyclic shearing compaction. Such phenomena are crucial to the compaction of asphalt mixtures or soils in civil engineering and can be extended to other areas, such as powder processing and pharmaceutical engineering. In this study, we investigated the behavior of both dry and fully saturated mono-dispersed granular materials under gyratory shearing compaction using the discrete element method (DEM) and found that the gyratory speed or interstitial fluid viscosity has almost no impact on the compaction behavior, while the pressure and the particle size play more important roles. Additionally, it is the inertial time scale which dictates the compaction behavior under gyratory shearing in most cases; meanwhile, the viscous time scale can also have influence in some conditions. This work determines the similarity and unity between the granular gyratory compaction and the rheology of granular systems, which has direct relevance to various natural and engineering systems.

Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving

We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework employs a modular architecture orchestrated by a meta-agent, which serves as the central coordinator, managing an action generator and instruction-tuned small-scale language models (expert models). The action generator decomposes complex problems into sub-tasks and identifies suitable expert models to execute each, delivering precise solutions for multi-step problem-solving. Key techniques include advanced knowledge modeling using property graphs for improved information retrieval, facilitating more accurate and contextually relevant solutions. Additionally, the framework utilizes a teacher-student transfer-learning approach with GPT-4 (Omni) to fine-tune the action generator and expert models for domain adaptation, alongside an iterative problem-solving mechanism with sophisticated error handling. Custom datasets were developed to evaluate the framework against leading proprietary language models on various engineering tasks. The results demonstrate the framework’s effectiveness in automating calculations, accelerating prototyping, and providing AI-augmented decision support for industrial processes, marking a significant advancement in process engineering capabilities.

Effects of Various Packaging Materials on Physico-Chemical Characteristics of Vacuum Fried Carrot Chips during Storage

Aims: The objective of this study was to determine the effects of type of packaging materials and time of storage on the physicochemical characteristics of vacuum fried carrot chips. Place and Duration of Study: The research work was carried out at the Dept. of Processing and Food Engineering (P&FE), Kelappaji College of Agricultural Engineering and Technology, Tavanur, Kerala during the year of 2019-20. Methodology: The vacuum fried carrot chips were packed into three different packaging materials namely low-density polyethylene stand pouch (LDPE), laminated aluminum flexible pouch and polypropylene. All three packaging materials were tested with and without N2 gas filling. The storage studies were conducted for the vacuum fried carrot chips which was fried under optimized processing conditions: Frying Temperature-100⁰C; Frying Pressure-13 kPa; and Frying Time- 20 min. The experimental study was conducted for 4 months period and data was recorded for every 30 days interval. The packed samples were stored at the ambient temperature (25±5ºC) and relative humidity (70±10%) for storage studies. The quality attributes such as moisture content, water activity, oil content, hardness and colour values of the chips were determined during the storage studies. Results: Based on the research study, the results shown that the oil content remained constant throughout 120 days of storage period in all packaging materials. The water activity, moisture content, hardness and colour values got increased with increase in storage time. The moisture content, water activity and hardness values were less in laminated aluminium pouches with N2 gas filling compared to polypropylene and LDPE.

Preface: 7th International Conference on Chemical Engineering and Advanced Materials (CEAM 2024)

The 2024 7th International Conference on Chemical Engineering and Advanced Materials (CEAM 2024) was held in Philadelphia, USA during September 28-29, 2024. This conference aims to provide opportunities for all participants to exchange new innovative ideas and application experiences as well as to provide a platform where possible innovators can think of new ventures, generate contacts and networks for future collaboration. The CEAM 2024 topics include chemical engineering, biological chemistry, biological pharmacy, food science, chemical materials, nano materials, materials processing engineering, and advanced materials applications. This conference brought together leaders from industry and academia to exchange and share their experiences, present research results, explore collaborations, and to spark new ideas, with the aim of developing new projects and exploiting new technology. On behalf of the conference organizers, we would like to thank the reviewers for their time and effort in reviewing the papers. Their feedbacks to the authors have been excellent and useful to ensure high quality papers for the conference. Our appreciation to all keynote speakers for sharing their thoughts and experiences with respect to various topics. Our sincere thanks also go to all attendees from all around the world for participating in the conference. Finally, we would like to thank the organizing committee members for their hard work, cooperation, and insights in ensuring the success of this conference. Scientific Committee of CEAM 2024 Philadelphia, USA

Multidisciplinary Framework for Vibration-Based Gear Fault Diagnosis Using Experiments, Modeling, and Machine Learning

Vibration-based gear diagnosis is crucial for ensuring the reliability of rotating machinery, making the monitoring of gear health essential for preventing costly downtime and optimizing performance. This study proposes a multidisciplinary framework to enhance gear diagnosis, that aligns with the new era of digital twins by integrating experiments, dynamic modeling, physical preprocessing, and machine learning. Within this framework, we focus on three core procedures: domain adaptation to reduce discrepancies between measured data and synthetic data generated by dynamic models; physical preprocessing, grounded in in-depth investigations dictating signal processing and feature engineering techniques; and learning algorithms, encompassing the process of training AI-based models. We demonstrate this framework through a comprehensive case study of localized tooth fault diagnosis, using controlled-degradation tests and realistic simulations. First, we detect faults using unsupervised learning algorithms; then, we use zero-shot-learning for classifying between localized and distributed faults; finally, we adopt a one-shot-learning strategy for severity estimation. Above all, this hybrid framework bridges the gaps between physical-based and AI-based approaches by combining physical knowledge and advanced algorithmics with machine learning. This contributes to the PHM field by offering valuable insights into integrating different aspects of research, thereby enhancing performance in gear diagnosis tasks.

Design and Technical-Economic-Environmental Evaluation of a Biorefinery Using Non-Marketable Edible Mushroom Waste

Edible mushrooms are gaining popularity as functional foods, not only for their distinctive taste and subtle flavor but also for their potential health benefits. They are rich in essential nutrients, including carbohydrates, proteins, fiber, minerals, and vitamins. However, it is estimated that up to 30% of total mushroom production generates non-marketable waste, which is currently used as animal feed, fertilizer, or compost. From a circular economy perspective, the objective of this work is to propose a conceptual design for a biorefinery to produce high-value-added products from non-marketable edible mushroom waste and to conduct a comprehensive assessment using modeling and simulation tools for process engineering. The general approach first involves identifying high-potential products, then determining the best processing routes, followed by developing a process flowsheet diagram for a case study (A. bisporus), and ultimately conducting a comprehensive assessment of the biorefinery to evaluate its sustainability. As a result, the proposed biorefinery may produce chitin, citric acid, and fertilizer, demonstrating the technical feasibility of waste valorization in terms of product yields, with positive profitability in terms of the rate of return on investment and payback period, and low environmental impact in terms of water and energy consumption as well as CO2 emissions.

Mid-infrared edge-enhanced imaging via angle-selective nonlinear filtering.

We propose a novel, to the best of our knowledge, scheme for mid-infrared upconversion imaging with high tunability between bright-field and edge-enhanced modalities. The involved engineering of the nonlinear process favors shaping the optical transfer function of the imaging system. Consequently, a nonlinear angle-selective filter can be configured to perform an all-optical Fourier processing of the image, which highly depends on phase-matching parameters. We numerically demonstrate the ability to switch modalities between the bright-field and edge-enhanced imaging by tuning the crystal temperature and simultaneously acquiring both information by dichromatic illumination. Notably, the achieved reconfigurability is realized without changing the imaging settings, which contrasts with previous instantiations based on pump adaptation. Therefore, the proposed architecture of upconversion imagers would pave a novel way to implement layout-compact and all-optical processing for infrared images.

Digital twins in process engineering: An overview on computational and numerical methods

Digital twins in process engineering: An overview on computational and numerical methods

Collision theory and membrane photocatalysis: Steroid hormones meet singlet oxygen in palladium-porphyrin-coated polytetrafluoroethylene membranes

Photocatalytic membrane (PCM) reactors are an emerging technology for the continuous elimination of micropollutants from water. PCM materials and process properties define performance limitations. A collision theory framework was established to elucidate the limiting factors of steroid hormone micropollutant photodegradation inside the pores (200 nm) of a palladium-porphyrin-coated polytetrafluoroethylene (PTFE) PCM under simulated sunlight. The collision theory can describe the degree of photodegradation of 17β-estradiol (E2). The production of singlet oxygen reactive species was limited by light intensity (up to 14 mW cm−2), porphyrin loading (up to 50 μmol/L), and membrane layer thickness (46 μm, achieved via stacking thin membranes). Further increases in these parameters did not significantly enhance the removal of E2, because the quantity of singlet oxygen generated, and consequently the collision frequency, levelled off. Not all collisions result in photodegradation reaction. By reducing the reaction time via increasing the E2 molar flux, the rate of disappearance reached a threshold of 7 ± 2 nmol/L s−1. This is identified as the maximum effective collision frequency, implying that 11 % of the total collisions (64 ± 5 nmol/L s−1) resulted in successful reaction. The study presents a novel framework based on collision theory to predict the fundamental mechanisms of and limitations to photodegradation in porphyrin-PTFE membranes, providing unprecedented insight into the performance limitations of diverse membrane reactors for advances in materials and process engineering.

Lipidation Engineering in Daptomycin Biosynthesis.

Lipopeptides are an important family of natural products, some of which are clinically used as antibiotics to treat multidrug-resistant pathogens. Although the lipid moieties play a crucial role in balancing antibacterial activity and hemolytic toxicity, modifying the lipid moieties has been challenging due to the complexity of the lipidation process in lipopeptide biosynthesis. Here, we show that the lipid profile can be altered by engineering both secondary and primary metabolisms, using daptomycin as an example. First, swapping the fatty acyl AMP ligase (FAAL) gene dptF with foreign FAAL homologs improved the fatty acyl specificity of the lipidation process for decanoic acid. Then, the introduction of Mycobacterium type I fatty acid synthase operon (MvFAS-Ib/MvAcpS) and Cryptosporidium thioesterase (CpTEII) enriched the fatty acid pool with decanoic acid in Streptomyces roseosporus. The engineered fatty acid metabolism eliminates the need for external decanoic acid supplementation by enabling S. roseosporus to biosynthesize decanoic acid. By complete engineering of the lipidation process, we achieved, for the first time, high-purity, natural production of daptomycin. The lipidation engineering approach we demonstrate here lays the foundation for the lipidation control in lipopeptide biosynthesis.

The 'Omics Revolution in CHO Biology: Roadmap to Improved CHO Productivity.

Chinese hamster ovary (CHO) cell physiology understanding has advanced very rapidly in the past few years with incredible improvements in long-read sequencing, improved resolution, and increased computational power. Multiple parental lines have been sequenced and the resultant pan-genome can be leveraged to increase our understanding of the diverse pathways CHO cells can take to get high-productivity phenotypes. The same improvements in workflows have complemented transcriptomic studies. Microfluidics and label-free innovations have further increased the sensitivity and accuracy of proteomic methods, while also making proteomics more accessible. In this 'omics era, high-throughput screening methods, sophisticated informatic tools, and models continually drive major innovations in cell line development and process engineering. This review describes the various recent achievements in 'omics techniques and their application to improve recombinant protein expression from CHO cell lines.

Lightweight skin cancer detection IP hardware implementation using cycle expansion and optimal computation arrays methods

Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early-stage skin lesions is imperative for accurate diagnosis. However, deploying these algorithms on low-cost devices and attaining high-efficiency operation with minimal energy consumption poses a formidable challenge due to their intricate computational demands. This study proposes a lightweight hardware design based on a convolutional neural network (CNN) for real-time processing of skin disease classifiers on portable devices. Our skin cancer recognition processor utilizes an optimally parallel designed processing engine (PE) for global computation, which greatly reduces hardware resource utilization by multiplexing of computational unit circuits. In addition, a design approach that provides loop unrolling effectively reduces the number of data accesses, thereby reducing computational complexity and logic resource requirements. The hardware circuits in this design perform data inference in convolutional, pooling, and fully connected layers based on 16-bit floating-point numbers. Evaluation of the HAM10000 database dataset shows that the architecture achieves an average classification accuracy of 97.8 %. We are the first to implement an all-hardware FPGA-based skin cancer detection platform that offers a 3.5x speedup in recognition compared to existing skin cancer accelerators at 50 MHz while consuming only 0.48 W of power. The implementation of this hardware architecture meets the major constraints of portable devices, featuring low resource utilization, low power consumption, and cost-effectiveness, while still providing efficient classification and high accuracy results.

Impact of Shelf life on the Quality of Cocoa Butter Extracted by Screw Press

Aims: To evaluate the changes in the quality of cocoa butter during storage extracted using the developed screw press-type cocoa butter extractor. Study Design: Statistical analysis was done using t-test. Place and Duration of Study: The study was conducted at Processing and Food Engineering lab, Kelappaji College of Agricultural Engineering and Food Technology, Tavanur under ICAR-AICRP on Post-Harvest Engineering and Technology Scheme for 3 months. Methodology: Shelf life study of the cocoa butter extracted at optimized condition (100ºC barrel temperature, 50 rpm screw speed, 2 kg/hr feed rate) was done in polyethylene pouch of 80 micron thickness and stored under ambient temperature (25±5°C) for a period of 3 months. Various quality parameters viz., free fatty acid, iodine number, peroxide number, moisture content and water activity of stored butter were determined at regular intervals of 30 days for 3 months. Results: Statistical analysis (t-test) showed that, there was no significant changes in the quality of cocoa butter during storage. The quality parameters of cocoa butter viz.,free fatty acid (1.28%), iodine number (34.74), peroxide value (1.685), moisture content (0.15%) and water activity (0.645) were within the allowable limits recommended by CODEX standards even after 3 months of storage. Conclusion: During the course of storage, all quality parameters viz., free fatty acid, peroxide value, iodine number, moisture content and water activity were found to be increased. However, all the quality parameters of cocoa butter stored under ambient temperature were within the acceptable limit prescribed by CODEX standards till 90 days with constant quality attributes.

Study on the Engineering Properties of Whole Garlic Bulbs and Garlic Cloves for Effective Design of Processing Machinery

The present experiment was conducted from February, 2024 to March, 2024 at the Department of Agricultural Process Engineering, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, Maharashtra, India to investigate the various engineering properties of garlic bulbs and garlic cloves. The engineering properties viz., physical, gravimetric and frictional properties, were studied for whole garlic bulbs and garlic cloves by considering their importance in developing different agricultural and post-harvest processing machinery. The weight 11.41 g, length (polar diameter) 33.69 mm, width (equatorial diameter) 29.51 mm, thickness 27.58 mm, arithmetic mean diameter 30.26 mm, geometric mean diameter 30.10 mm, shape index 0.88, sphericity 0.90, surface area 63.05 cm2, cross-sectional area 53.75 cm2, bulk density 449.50 kg m-3, true density 930.45 kg m-3, porosity 51.69%, moisture content 56.70% and angle of repose of garlic bulb 50.54° were obtained. Similar properties were determined for garlic cloves. The L, a and b values for garlic bulb and garlic cloves were 82.95, 0.23, 10.66 and 74.42, 0.28, 12.48, respectively. The water activity of garlic was found to be 0.98±0.02. The coefficient of friction of the garlic bulb and garlic cloves was measured for the selected testing surfaces such as mild steel (MS), stainless steel (SS), wood, glass and fiberglass which was considered in hopper design. The compression force required to loosen the cloves from the garlic bulb was calculated as 61 N. By integrating these engineering properties into the design process, manufacturers can develop equipments or machineries that are more efficient, durable and capable of producing high-quality garlic products.

Efficient Simulation of Viral Transduction and Propagation for Biomanufacturing.

The design of biomanufacturing platforms based on viral transduction and/or propagation poses significant challenges at the intersection between synthetic biology and process engineering. This paper introduces vitraPro, a software toolkit composed of a multiscale model and an efficient numeric technique that can be leveraged for determining genetic and process designs that optimize transduction-based biomanufacturing platforms and viral amplification processes. Viral infection and propagation for up to two viruses simultaneously can be simulated through the model, considering viruses in either the lytic or lysogenic stage, during batch, perfusion, or continuous operation. The model estimates the distribution of the viral genome(s) copy number in the cell population, which is an indicator of transduction efficiency and viral genome stability. The infection age distribution of the infected cells is also calculated, indicating how many cells are in an infection stage compatible with recombinant product expression or viral amplification. The model can also consider the presence of defective interfering particles in the system, which can severely compromise the productivity of biomanufacturing processes. Model benchmarking and validation are demonstrated for case studies of the baculovirus expression vector system and influenza A propagation in suspension cultures.

Optimizing Motorcycle Manufacturing Sustainability through the Integration of Waste Heat Recovery and Metal Scrap Recycling: A Process Engineering Approach

The automotive industry manufacturing has experienced rapid growth 2–3 times by 2050, with motorcycles constituting around 30% of vehicles worldwide, but this increase in production has significantly heightened the demand for raw materials and energy. A major challenge arises in managing material waste and waste heat generated during the manufacturing process. This research aims to develop a framework that optimizes the synergy between material waste recycling and waste heat recovery to enhance the sustainability of the motorcycle industry, reduce waste, and lower energy consumption. The design leverages waste heat from the melting process to preheat raw materials, raising temperatures from around 50 °C to 350 °C before melting, thereby reducing additional energy needs, lowering emissions, and decreasing operational costs. Utilizing waste heat for preheating not only mitigates environmental impact and thermal load but also significantly improves energy efficiency, ultimately resulting in cost savings and optimized resource use. Utilizing waste heat directly for preheating raw materials has effectively lowered energy consumption by as much as 30%. This approach not only improves operational efficiency but also decreases production costs and minimizes environmental impact, offering a more sustainable solution for the manufacturing sector.

Treatment Recommendation using BERT Personalization

This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named `PharmaBERT`, includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs and fine-tunes the BERT structure to perceive lots of possible sentiments for specific medications. Consequently, PharmaBERT brings all its training and optimization capabilities together and through this, the system reaches a higher accuracy of up to 91% thus showcasing the potency of the model in capturing patient sentiments. While being a BERT spin-off, PharmaBERT utilizes its own set of experienced techniques to comprehend and sense the health-related text input given by the patient, doctor, or pharmacist. It uses transfer learning, that is, it learns from language representations to adapt quickly to the intricacies of drug reviewing. Through PharmaBERT, healthcare professionals may expand their diagnoses with insights from patient feedback to constitute more neutral decisions.

Long-time prediction of multi-indicator water quality based on the improved WaveNet under time series decomposition and phase space reconstruction

Abstract Long–time prediction of water quality indicator such as chlorophyll–a (Chl–a) is crucial for water process engineering and environmental management. In order to capture the characteristics of long–time series and reduce the limitations of traditional long–time prediction strategies, this paper proposes a novel hybrid model by combining data decomposition, phase space reconstruction, feature fusion and improved WaveNet. Firstly, the original data is decomposed into several subsequences through time series decomposition. Then, the subsequences with chaotic characteristics are integrated with multiple features for phase space reconstruction. Next, the decomposed and reconstructed subsequences are fed back into the improved WaveNet model separately. Finally, the prediction results are obtained by summing the predicted values of the subsequences. In this paper, the reliability of the method is assessed using the dissolved oxygen, water temperature, pH and Chl–a data of a monitoring station in the Beihai coastal sea area, ablation experiments are conducted to demonstrate the effectiveness of each module in the proposed model, and comparisons with multiple benchmark and hybrid models show that the proposed model exhibits better performance in long–time prediction of coastal water quality in the next fourteen days.

Biosurfactants: Chemical Properties, Ecofriendly Environmental Applications, and Uses in the Industrial Energy Sector

The exploitation of nature and the increase in manufacturing production are the cause of major environmental concerns, and considerable efforts are needed to resolve such issues. Oil and petroleum derivatives constitute the primary energy sources used in industries. However, the transportation and use of these products have huge environmental impacts. A significant issue with oil-related pollution is that hydrocarbons are highly toxic and have low biodegradability, posing a risk to ecosystems and biodiversity. Thus, there has been growing interest in the use of renewable compounds from natural sources. Biosurfactants are amphipathic microbial biomolecules emerging as sustainable alternatives with beneficial characteristics, including biodegradability and low toxicity. Biosurfactants and biosurfactant-producing microorganisms serve as an ecologically correct bioremediation strategy for ecosystems polluted by hydrocarbons. Moreover, synthetic surfactants can constitute additional recalcitrant contaminants introduced into the environment, leading to undesirable outcomes. The replacement of synthetic surfactants with biosurfactants can help solve such problems. Thus, there has been growing interest in the use of biosurfactants in a broad gamut of industrial sectors. The purpose of this review was to furnish a comprehensive view of biosurfactants, classifications, properties, and applications in the environmental and energy fields. In particular, practical applications of biosurfactants in environmental remediation are discussed, with special focus on bioremediation, removal of heavy metals, phytoremediation, microbial enhanced oil recovery, metal corrosion inhibition, and improvements in agriculture. The review also describes innovating decontamination methods, including nanobioremediation, use of genetically modified microorganisms, enzymatic bioremediation, modeling and prototyping, biotechnology, and process engineering. Research patents and market prospects are also discussed to illustrate trends in environmental and industrial applications of biosurfactants.