Engineering Process Research Articles

Bio-mediated synthesis of nanoparticles: A new paradigm for environmental sustainability

The synthesis of nanoscale metals and non-metals is an intriguing subject, and the green synthesis of nanoparticles (NPs) is increasingly utilized across various sectors, including environmental science, agriculture, engineering, and food processing. Traditionally, the production of nanoscale materials relies heavily on physical and chemical processes, which can lead to significant challenges such as high energy consumption and environmental contamination. Poor management of agricultural and industrial waste contributes to greenhouse gas emissions, exacerbates climate change and disrupts ecosystems. Conversely, green nanotechnology offers a safer alternative by leveraging biological materials, that inherently provide capping and reducing agents. This approach is not only more cost-effective but also results in lower pollution levels, thereby enhancing environmental safety. Green synthesis involves the reduction of metallic and non-metallic atoms using plant extracts, microorganisms, and agricultural waste instead of conventional harmful substances. The bioactive compounds, including flavonoids, alkaloids, tannins, and saponins, play a critical role in the bioreduction of metals and the production of nanoparticles. There has been the increasing interest in utilizing these biological sources for green nanoparticle production over the past decade from their potential to serve as economical and environmentally friendly alternatives. Overall, green nanotechnology demonstrates its potential to revolutionize industries and pave the way for a more sustainable and resilient future.

Agile Construction Digital Twin Engineering

Digital twins have attracted a lot of attention recently. However, the current manifestations are merely digital shadows, lacking means for bidirectional data exchange, which makes their use for assisting the construction of buildings much more difficult. We argue that this is due to the lack of a systematic process for developing a digital twin during a building’s life cycle. We argue to look for a solution by combining agile engineering with IT change management to establish an agile, change-driven process for engineering digital twins. Such a process, of course, deserves a qualitative assessment of the engineering process and the resulting digital twin. In the future, it should be possible to obtain a digital twin from a BIM-based design process by applying IT change management in an agile manner. This should happen under maximum automation and life cycle orientation. Our proposal is motivated by several years of interdisciplinary collaboration between civil engineering and computer science and evaluated using the Technology Acceptance Model. While the TAM is not specifically designed for digital twin methodologies, its application here aims to assess perceived usefulness and ease of use of DT methodologies from the user’s perspective, without addressing scalability concerns. This aims to provide actionable insights to guide the refinement of the process model, aligning it with user requirements and achieving its intended outcomes. Our evaluation confirms the proposed process’s perceived usefulness and ease of use, with robust correlations indicating strong acceptance potential among stakeholders. These results highlight the feasibility of the proposed approach and its alignment with expectations in real-world applications.

Ultrasonic Sensor Modeling with Support Vector Regression

This study proposes a novel approach for predicting the output behaviors of the Pepperl+Fuchs 3RG6232-3JS00-PF ultrasonic sensor. The sensor, integrated into the Festo MPS-PA Didactic System, serves to monitor the water level in a tank, facilitating water extraction to bottles delivered via a conveyor belt. This modeling approach represents the initial phase in the creation of a digital twin of the physical sensor, providing the capability for users to observe the sensor’s response and forecast its life cycle for maintenance objectives. This study utilizes the Festo MPS-PA Compact Didactic System and support vector regression (SVR) for data acquisition (DAQ), preprocessing, and model training with hyperparameter optimization. The objective of this modeling approach is to establish a digital framework for transition towards Industry 4.0. It holds the potential for creating a digital counterpart of the entire MPS-PA System when combining the proposed sensor modeling technique with computer-assisted design (CAD) software such as Siemens NX in the future. This would enable users to oversee the entire process in a three-dimensional visualization engine, such as Tecnomatix Plant Simulation. This research significantly contributes to the comprehension and application of digital twins in the realm of mechatronics and sensor systems technology. It also underscores the importance of digital twins in enhancing the efficiency and predictability of sensor systems. The method used in this paper involves predicting the rate of change (RoC) of the water level and then integrating this rate to estimate the actual water level, providing a robust approach for sensor data modeling and digital twin creation. The result shows a promising 6.99% error percentage.

A Strategy for Simultaneous Engineering of Interspecies Cross-Reactivity, Thermostability, and Expression of a Bispecific 5T4 x CD3 DART® Molecule for Treatment of Solid Tumors.

Background: Bispecific antibodies represent a promising class of biologics for cancer treatment. However, their dual specificity and complex structure pose challenges in the engineering process, often resulting in molecules with good functional but poor physicochemical properties. Method: To overcome limitations in the properties of an anti-5T4 x anti-CD3 (α5T4 x αCD3) DART molecule, a phage-display method was developed, which succeeded in simultaneously engineering cross-reactivity to the cynomolgus 5T4 ortholog, improving thermostability and the elevating expression level. Results: This approach generated multiple DART molecules that exhibited significant improvements in all three properties. The lead DART molecule demonstrated potent in vitro and in vivo anti-tumor activity. Although its clearance in human FcRn-transgenic mice was comparable to that of the parental molecule, faster clearance was observed in cynomolgus monkeys. The lead α5T4 x αCD3 DART molecule displayed no evidence of off-target binding or polyspecificity, suggesting that the increased affinity for the target may account for its accelerated clearance in cynomolgus monkeys. Conclusions: This may reflect target-mediated drug disposition (TMDD), a potential limitation of targeting 5T4, despite its limited expression in healthy tissues.

Experimental study on pressure relief blasting in rock-blast tunnel

Rock burst is a difficult and urgent problem during the construction process of underground engineering in a zone with high surrounding rock pressure. Pressure relief blasting is one of the most effective methods for solving the problem. An in-situ blasting test was carried out in a tunnel under excavation, and the geology stress (geo-stress) of the surrounding rock was tested before and after the pressure relief blasting. The experimental results will be effective for further study of the law of pressure relief blasting and rock-burst prediction. An explicit stress analytic function is also employed to analyze the stress distribution of the tunnel surrounding rock after pressure relief blasting based on the propagation of the blasting stress wave. Furthermore, the stress attenuation coefficient and the variation of the geo-stress distribution of surrounding rock under the pressure relief blasting were discussed in detail with the in-situ blasting test data. Highlights. The main contribution of this paper is focused on: 1. This study measures the in-situ geo-stress of surrounding rock in the ‘Sang zhu ling’ tunnel, both before and after the pressure relief blasting. This data is crucial for ensuring the safety resilience of urban infrastructure. 2. A two-dimensional explicit equation of stress attenuation was derived based on the characteristics of stress propagation, providing a more accurate analytical tool for engineers and researchers, 3. The traditionally obtained stress attenuation coefficient is shown to require modification through further in-situ experiments. Future studies will refine this coefficient, contributing to the development of more reliable methods for safeguarding urban lifelines. These contributions align with the journal’s focus on advancing urban science and engineering by promoting innovative, data-driven solutions for urban safety.

Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength

Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in artificial neural networks (ANNs) have shown promise in simplifying this task by estimating it with high accuracy. Nevertheless, conventional ANNs often require deep networks to achieve acceptable results in cases with large datasets and where generalization is required for a variety of mixtures. This leads to increased training durations and susceptibility to noise, causing reduced accuracy and potential information loss in deep networks. In order to address these limitations, this study introduces a novel multi-lobar artificial neural network (MLANN) architecture inspired by the brain’s lobar processing of sensory information, aiming to improve the accuracy and efficiency of estimating concrete compressive strength. The MLANN framework employs various architectures of hidden layers, referred to as “lobes,” each with a unique arrangement of neurons to optimize data processing, reduce training noise, and expedite training time. Within the study context, an MLANN is developed, and its performance is evaluated to predict the compressive strength of concrete. Moreover, it is compared against two traditional cases, ANN and ensemble learning neural networks (ELNN). The study results indicated that the MLANN architecture significantly improves the estimation performance, reducing the root mean square error by up to 32.9% and mean absolute error by up to 25.9% while also enhancing the A20 index by up to 17.9%, ensuring a more robust and generalizable model. This advancement in model refinement can ultimately enhance the design and analysis processes in civil engineering, leading to more reliable and cost-effective construction practices.

Effects of Heat Transport Characteristics and Chemical Reaction in Unsteady Flow of Williamson Fluid and Entropy Generation: The Keller‐Box Numerical Scheme

ABSTRACTThe study of heat and mass transport in non‐Newtonian fluid flow over a stretching surface accompanying relevant characteristics is important in several engineering and industrial processes like annealing and thinning of copper wires, aerodynamic extrusion of plastic and rubber sheet, glass fiber, and so forth. Based on significant practical applications, the objective of this investigation is to assess the time‐dependent flow of Williamson fluid influenced by porous sheet stretching in exponential manner accompanied by thermal and mass transport and entropy generation. Various factors affecting fluid flow, thermal and mass transport (viscous dissipation, non‐linear radiation, porous media, chemical reaction, and heat source) are considered. The regulating PDEs are turned into ODEs in nondimensional form utilizing adequate similarity transformation relations. The problem is solved numerically on MATLAB adopting the Keller‐Box scheme. On fluid flow, temperature, and concentration distribution the effects of relevant parameters are depicted by drawing sketches and discussed. Besides, second law analysis is also evoked in the study in terms of entropy generation accompanying the Bejan number. Moreover, quantities of physical significance such as skin friction coefficient, Sherwood number, and Nusselt number are computed, compared with prior research and found in excellent agreement. It is concluded that temperature profile magnifies due to radiation and heat generation effects. The reaction coefficient and order of the reaction exhibited opposite effects on concentration profile. It is also concluded that entropy production reduces with increasing slips and temperature difference parameter, while opposite effect is observed due to Brinkman number. Furthermore, it is observed that skin‐friction coefficient at the surface decreases with velocity slip and non‐Newtonian parameter however, trend is reversed due to unsteadiness parameter. The results of the study may find applications of practical importance in engineering fields such as designing heat exchangers, cooling processes, improving energy storage systems, and so forth.

Superfast nanodroplet propulsion in 2D nanochannels tuned by strain gradients.

Directional transport of droplets is crucial for industrial applications and chemical engineering processes, with significant potential demonstrated in water harvesting, microfluidics, and heat transfer. In this work, we present a novel approach to induce self-driving behavior in nanodroplets within a two-dimensional (2D) nanochannel using a strain gradient, as demonstrated through molecular dynamics simulations. Our findings reveal that a small strain gradient imposed along a nanochannel constructed by parallel surfaces can induce water transport at ultrafast velocities (O(102 m s-1)), far exceeding macroscale predictions. Certainly, a larger strain gradient further enhances droplet transport velocity. Additionally, combining a strain gradient with nonparallel surfaces results in up to a 150% increase in transport efficiency. Furthermore, we show that this spontaneous transport mechanism is applicable to nanochannels composed of various 2D materials and successfully establish a reliable theoretical model. These simulation results provide new insights into the directional transport of nanodroplets in 2D nanochannels, opening avenues for advanced applications in nanotechnology and fluid dynamics.

Amino Acids Frequency and Interaction Trends: Comprehensive Analysis of Experimentally Validated Viral Antigen-Antibody Complexes.

Antibodies have specific binding capabilities and therapeutic potential for treating various diseases, including viral infections. The amino acid composition of the hypervariable complementarity determining regions (CDR) loops and the framework regions (FR) are the determining factors for the affinity and therapeutic efficacy of the antibodies. In this study selected and curated, 77 viral antigen-human antibody complexes from Protein data bank from the Thera-SAbdab database were analyzed. The results revealed diversity indices within specific CDR regions, amino acid frequencies, paratope-epitope interactions, bond formations, and bond types among the analyzed viral Ag-Ab complexes. The finding revealed that Ser, Gly, Tyr, Thr, and Phe are prominently present in the antibody CDRs. Analysis of CDR profiles indicated an average amino acid diversity of 60-80% in heavy chain CDRs and 45-60% in light chain CDRs. Aromatic residues, particularly Tyr, Phe, and Trp showed significant involvement in the paratope-epitope interactions in the heavy chain, while Tyr, Ser, and Thr were key contributors in the light chain. Furthermore, the study examined the occurrence of amino acids in both light and heavy chains of viral Ag- human Ab complexes, analyzing the presence of amino acids as single residues, dipeptides and tripeptides. The analysis is crucial for enhancing the antibody engineering processes including, design, optimization, affinity enhancement, and overall antibody development.

The influence of hydrogen injection position on the combustion process of a hydrogen direct injection X-type rotary engine with biased combustion chamber

The influence of hydrogen injection position on the combustion process of a hydrogen direct injection X-type rotary engine with biased combustion chamber

Computational Approaches to Solving Some Partial Differential Equations and Neutrosophic Partial Differential with Variable Coefficients Using the Laplace Residual Power Series Method

We employ the Laplace Residual Power Series Method to approximate analytical solutions for differential equations and neutrosophic differential equations with associated parameters, including non-homogeneous equations and fractional formulas in partial differential equations (PDEs). This approach showcases the method's simplicity, effectiveness, and robustness in deriving analytical series solutions for PDEs that involve associated parameters, especially in the context of fractional differential equations. Several practical uses of LRPSM with an emphasis on non-homogeneous and partial differential equations and neutrosophic equations with fractions (PDEs). These applications are significant in a variety of scientific and engineering domains that simulate complicated dynamic system such as anomalous diffusion in physics, viscoelastic material modeling in engineering and signal processing.

Modeling shear‐induced flow dynamics in a thermal Riga channel containing radioactive rGO‐magnetite‐mercury in an intense electromagnetic rotational setting

AbstractThe main goal of our study is to examine the shear‐induced flow dynamics of a hybrid nanofluid (HNF) composed of rGO/magnetite‐mercury within a thermal vertical Riga channel in an intense electromagnetic rotational framework, invoking the existence of Hall and ion‐slip currents. The model configuration involves a static right wall and a left wall undergoing either impulsive motion (IM) or accelerated motion (AM), initiating fluid movement, which is mathematically represented by unsteady partial differential equations. The Laplace transform (LT) method is harnessed to get a closed‐form solution for the flow‐regulating equations. Through graphical representations, we detail the dominance of critical parameters on model functions and quantities for both IM and AM scenarios. Our key findings admit that an upswing in rotation and the modified Hartmann number significantly diminishes velocity components in both IM and AM cases. The primary velocity experiences a notable diminution with an amplification in the Hall and ion‐slip parameters, while the secondary velocity's magnitude strengthens. Primary and secondary velocities are consistently higher in IM compared to AM. A heightened modified Hartmann number reduces shear stresses at the moving wall due to primary flow in both IM and AM scenarios. Additionally, the magnitude of shear stresses at the moving wall is consistently more pronounced in IM than in AM, with shear stresses notably higher in IM. As the radiation parameter grows, the rate of heat transfer RHT at both channel walls diminishes. Moreover, rate of heat transfer for the HNF consistently exceeds that for the nanofluid (NF). The novelty of the study lies in its unique combination of a radioactive HNF, the thermal Riga channel, and electromagnetic rotational effects, providing new insights into the flow dynamics under extreme conditions, with potential applications in energy systems, nuclear reactor technology, spacecraft propulsion, satellite operations, space exploration, aerospace engineering, chemical mixing, and materials processing.

Analytical approach to control the irreversibilities in a chemically reactive nanofluid flow over a Darcy–Forchheimer medium with nonuniform heat source/sink

AbstractIn real‐world processes, such as heat transfer, fluid flow, and chemical reactions, irreversibility occurs frequently, which induces an augment in entropy. The optimization of the entropy generation in a mechanical system is one of the fundamental areas of research to channel the energy of the system for utilization. This research exhibits an entropy generation in a nanofluid flow drenched in a fluid‐saturated non‐Darcy porous medium toward a stagnation point. The flow line also experiences the existence of nonuniform heat generation/absorption following the first‐order chemical reaction, which enhances the applicability of this model in different domains of science and engineering. A special form of the Lie group approach is applied to revert the governing partial differential system into its self‐similar ordinary differential form. The solutions of the transformed nonlinear ODEs are acquired analytically through the DTM ‐Padé as well as numerically by the Runge–Kutta–Fehlberg (RKF‐45) method coupled with the shooting process. The fallouts are vividly sketched graphically. One of the upshots reveals that the escalation of space and temperature‐reliant heat absorption parameters can optimize the thermal irreversibility of the system and so as the total entropy generation. Meanwhile, by incrementing the Darcy parameter from 0 to 0.8, the wall shear stress decays by 23.7%, whereas with the same hike in the Forchheimer resistance factor, declines the rate of heat and mass transfer approximately by 2.6% and 3.6%, respectively. Another upshot reveals that with the escalation of the space‐dependent heat source parameter from 0.1 to 0.5, the Nusselt number significantly decreased by 23.9%. Meanwhile boosting the temperature‐reliant heat sink parameter from ‐0.1 to ‐0.5 causes an inclination in the rate of heat transportation by 10.5%. Moreover, it is found that mass transport irreversibility can be controlled by enhancing the constructive chemical reaction rate. We hope that this study will be beneficial for many engineering and industrial processes, particularly in geothermal energy extraction, nuclear waste disposal management, groundwater filtration machinery and food processing equipment.

CIFR (Clone-Integrate-Flip-out-Repeat): A toolset for iterative genome and pathway engineering of Gram-negative bacteria.

Advanced genome engineering enables precise and customizable modifications of bacterial species, and toolsets that exhibit broad-host compatibility are particularly valued owing to their portability. Tn5 transposon vectors have been widely used to establish random integrations of desired DNA sequences into bacterial genomes. However, the iteration of the procedure remains challenging because of the limited availability and reusability of selection markers. We addressed this challenge with CIFR, a mini-Tn5 integration system tailored for iterative genome engineering. The pCIFR vectors incorporate attP and attB sites flanking an antibiotic resistance marker used to select for the insertion. Subsequent removal of antibiotic determinants is facilitated by the Bxb1 integrase paired to a user-friendly counter-selection marker, both encoded in auxiliary plasmids. CIFR delivers engineered strains harboring stable DNA insertions and free of any antibiotic resistance cassette, allowing for the reusability of the tool. The system was validated in Pseudomonas putida, Escherichia coli, and Cupriavidus necator, underscoring its portability across diverse industrially relevant hosts. The CIFR toolbox was calibrated through combinatorial integrations of chromoprotein genes in P. putida, generating strains displaying a diverse color palette. We also introduced a carotenoid biosynthesis pathway in P. putida in a two-step engineering process, showcasing the potential of the tool for pathway balancing. The broad utility of the CIFR toolbox expands the toolkit for metabolic engineering, allowing for the construction of complex phenotypes while opening new possibilities in bacterial genetic manipulations.

A Multi-Objectives Optimization Model for the Joint Design of Statistical Process Control and Engineering Process Control

A Multi-Objectives Optimization Model for the Joint Design of Statistical Process Control and Engineering Process Control

Machine Learning Based Insights for Metal-Organic Frameworks Synthesis: A Comparative and Explainable Analysis of ZIF-8 Morphology

Metal-organic frameworks (MOFs) such as zeolitic imidazolate framework-8 (ZIF-8) are promising nanomaterials for various applications like drug delivery and energy storage. The efficacy of ZIF-8 in these applications highly depends on its morphology, including size and shape. However, understanding and controlling morphology during synthesis is challenging due to the complex interactions among synthesis conditions such as precursor concentration and reaction temperature. Traditional trial-and-error methods for morphology optimization are inefficient and cannot effectively account for the combined effects of conditions. Machine learning (ML) offers a powerful alternative for morphology prediction, which can accelerate the reverse engineering process to better understand how synthesis conditions affect morphology. Despite recent advances, developing accurate ML models and selecting the appropriate ones for specific applications remain a challenge. This study addresses these issues by experimentally investigating how variations in synthesis conditions, such as precursor concentrations, solvent properties, and temperature, affect ZIF-8 morphology. Using experimental data, this work further built and compared three ML models: Random Forest (RF), Support Vector Regressor (SVR), and Neural Network (NN). Among these, the NN model has the best performance in terms of R-squared and mean squared errors. These ML models provide insights into how synthesis conditions affect ZIF-8, thus setting the basis for future studies aimed at optimizing conditions and guiding more efficient manufacturing strategy to expand the applications of this versatile nanomaterial.

Forecasting Methanol-to-olefins Product Yields based on Relevance Vector Machine with Hybrid Kernel and Rolling-windows

Light olefins (ethylene and propylene) have become prominent in chemical industries. Forecasting of the yields of light olefins plays a crucial role in monitoring and optimizing the Methanol-to-olefins (MTO) process. In this work, we introduce an approach for forecasting the yields of ethylene and propylene in the MTO process with the Relevance Vector Machine (RVM) model, which is uniquely enhanced with hybrid kernels and a rolling window methodology. Through an in-depth analysis of 32 independent variables and their pairwise differences, our research pinpoints temperature and pressure as the most critical factors influencing the yields of ethylene and propylene, respectively. The model showcases satisfactory predictive accuracy and reasonable interpretability compared with the traditional statistical and popular machine learning models, marking a step forward in the predictive modeling of chemical engineering processes.

Analysis of learning strategies for subjects of the generic structure of the industrial engineering degree, in a higher education institution in Villahermosa Tabasco

The objective of this project is to analyze the learning strategies currently used by teachers in spatiality subjects, of the Industrial Engineering Degree, in a Higher Education Institution in Villahermosa Tabasco. A diagnosis will be made, designing an instrument through a Questionnaire, using the Likert scale. The instrument will be applied to Teachers who teach subjects in the Basic Structure. There is a population of 41 teachers in Industrial Engineering, of which there are 25 who are from the Basic Structure, focusing for this study on the Subject of Quality Systems Management of which there are 3 groups. The information will be analyzed immediately. From the results obtained, a proposal for new Learning strategies will be designed that will be of great contribution to improve the teachinglearning process of Industrial Engineering and the Institution.

Intelligent Control Systems for Mechanical Engineering Technology Tasks

The article is devoted to solving the main tasks set in the work with the aim of analyzing and substantiating the implementation of intelligent control systems in technological processes of mechanical engineering, with an emphasis on increasing efficiency, accuracy, and reliability of production. The use of multi-agent systems and decentralized control systems, which significantly enhance the flexibility and adaptability of production, is analyzed. Special attention is paid to the role of physics-informed neural networks in fault diagnosis, which ensures increased reliability and reduced maintenance costs for equipment. The effectiveness of applying machine learning algorithms to optimize production processes, particularly in material processing and equipment maintenance, is evaluated. The impact of integrating intelligent control systems on production performance and quality, especially in the processes of milling and bonding large parts, is considered. Practical recommendations have been developed for the implementation of an adaptive intelligent production management system (AIPMS), which combines multi-agent systems, neural networks, digital twins, and innovative materials. The implementation of the artificial intelligence concept in production processes will contribute to the further development of mechanical engineering from a technological perspective, enabling enterprises to adopt innovations more rapidly, increase automation, and enhance the adaptability of technological processes, which in turn will lead to significant improvements in product quality and competitiveness. The use of such systems allows optimizing technological processes, reducing the number of defects, lowering energy consumption, and improving environmental efficiency

IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning

The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices.