Knowledge-based Engineering Research Articles

Enhancing PVT property predictions for black oil reservoirs through the application of supervised machine learning techniques

The study presents an innovative approach utilizing machine learning (ML) techniques to forecast essential characteristics of black oil reservoirs, namely the oil formation volume factor (Bo) and the solution gas-oil ratio (Rs). A suite of four established ML algorithms-Random Forest, Linear Regression, Ada Boost, and Gradient Boost were extensively conditioned using a dataset that includes 297 individual data points and evaluation is done using test data and a blind test of models. The study showcases the practical efficacy of ML in accurately predicting Bo and Rs, even with a relatively modest dataset. The potential of ML models to save time and costs by reducing the dependence on laborious laboratory measurements is underscored, presenting a transformative shift in reservoir engineering practices. Furthermore, the current research extends its impact by comparing ML predictions with various empirical correlations and actual values, demonstrating that ML models, particularly Gradient Boost, consistently outperform traditional correlations and it can be used to predict values in the undersaturated region too. It is observed from the blind test results that for the prediction of Bo Gradient boost and random forest models (R2 0.97, RMSE 0.01) is a preferable choice and for the prediction of Rs the Gradient boost model (R2 0.97, RMSE 22) can be preferred. The adaptability of ML models to integrate new data over time is a significant feature, ensuring continuous improvement in accuracy. This study adds to the expanding reservoir engineering knowledge base, highlighting the practical use of machine learning for accurately predicting crucial reservoir properties. Its significance goes beyond theoretical progress, providing tangible tools for reservoir engineers and geologists to improve decision-making in the management of oil and gas reservoirs.

Artificial Intelligence Supporting Early Automotive Engineering Processes

Abstract Due to progressive increase of complexity, the automotive industry is subject to constantly changing trends varying from the introduction of greener products and components to the deployment of technological advances in development and engineering processes. In relation to both, sustainable automotive products as well as the deployment of technological advances, the integration of AI (Artificial Intelligence) approaches in combination with virtual products in automotive development and engineering processes is of great importance. In combination with knowledge-based CAx (Computer-Aided engineering), the integration of AI approaches delivers an enormous potential to enhance automotive development processes. In addition to process optimizations, the integration of various AI approaches considers sustainability (e.g., optimization of component geometries and materials, reduction of emissions over the entire life cycle, CO2 reduction through improved development) and economical aspects (e.g., resources savings throughout the entire development process, time and cost savings through earlier error detection, avoidance of unnecessary process steps). The present approach deals with the integration of AI and knowledge-based engineering methods in the early phases of automotive development and engineering processes. Furthermore, the paper points to the time, cost and resources reduction potential, leading to earlier market entries and a greener industry. Finally, the paper demonstrates the integration of AI technologies into industrial development and engineering processes based on selected application scenarios.

Innovating Library Services: Co-Creation, Experimentation, and Enhanced Business Value Tool for Technological Advancements

ABSTRACT This article provides entrepreneurs with information about library services and investigates the function of an Enhanced Business Value Calculator in identifying patrons who may be interested in participating in co-creation-driven experimental technology adoptions in libraries. By identifying the most productive users, the librarian could invite them to participate in the co-creation process, which results in more collaborative and knowledge-based technology experimentations. Patrons (high-performing users and their peers) actively participate in the design, execution, and post-experimentation phases of the experimentation process continuously through co-creation. Ongoing participation increases patron engagement, transforming patrons into library evangelists who support cooperative trials that result in novel services. The article culminates in a case study that shows how the public libraries in collaboration with the university library strategically experimented with ChatGPT to see if it might be used for its systematic literature review services for the entrepreneurs.

Artificial Intelligence (AI) and Knowledge-Based Engineering (KBE) in Ship Design: Bridging Tradition and Technology Through ACQUAINT

_ Despite the limited use of artificial intelligence/knowledge-based engineering (AI/KBE) in industries with small series or one-off designs, our study demonstrates the technical feasibility and potential benefits of implementing AI/KBE in ship design processes. This research presents the development of “ACQUAINT,” uniquely designed to address the complexities inherent in bespoke shipbuilding. Central to this module is a robust AI-driven inference engine, integrated seamlessly with AutoCAD through a Python-based interface, facilitating a novel approach in shipbuilding’s detail and production design phases. The module’s capability to generate optimal designs autonomously—grounded in a deep understanding of design rules, constraints, and requirements—substantially reduces the reliance on human interaction. Our initial proof of concept with “ACQUAINT” showcases measurable advancements in ship design accuracy and efficiency, highlighting AI KBE’s transformative impact in shipbuilding and setting a foundation for future research and practical applications. Keywords ship design; artificial intelligence; knowledge-based engineering; self-learning system; software development; ACQUAINT; CAD/CAM software; computers in construction; computers in design; modernization; ship structure

Proteomics and metabolic burden analysis to understand the impact of recombinant protein production in E. coli

The impact of recombinant protein production (RPP) on host cells and the metabolic burden associated with it undermine the efficiency of the production system. This study utilized proteomics to investigate the dynamics of parent and recombinant cells induced at different time points for RPP. The results revealed significant changes in both transcriptional and translational machinery that may have impacted the metabolic burden, growth rate of the culture and the RPP. The timing of protein synthesis induction also played a critical role in the fate of the recombinant protein within the host cell, affecting protein and product yield. The study identified significant differences in the expression of proteins involved in fatty acid and lipid biosynthesis pathways between two E. coli host strains (M15 and DH5⍺), with the E. coli M15 strain demonstrating superior expression characteristics for the recombinant protein. Overall, these findings contribute to the knowledge base for rational strain engineering for optimized recombinant protein production.

Digital Twin Modeling for Smart Injection Molding

In traditional injection molding, each level of the process has its own monitoring and improvement initiatives. But in the upcoming industrial revolution, it is important to establish connections and communication among all stages, as changes in one stage might have an impact on others. To address this issue, digital twins (DTs) are introduced as virtual models that replicate the entire injection molding process. This paper focuses on the data and technology needed to build a DT model for injection molding. Each stage can have its own DT, which are integrated into a comprehensive model of the process. DTs enable the smart automation of production processes and data collection, reducing manual efforts in supervising and controlling production systems. However, implementing DTs is challenging and requires effort for conception and integration with the represented systems. To mitigate this, the current work presents a model for systematic knowledge-based engineering for the DTs of injection molding. This model includes fault detection systems, 3D printing, and system integration to automate development activities. Based on knowledge engineering, data analysis, and data mapping, the proposed DT model allows fault detection, prognostic maintenance, and predictive manufacturing.

Exploring Biosensors' Scientific Production and Research Patterns: A Bibliometric Analysis.

More sustainable biosensor production is growing in importance, allowing for the development of technological solutions for several industries, such as those in the health, chemical, and food sectors. Tracking the latest advancements in biosensors' scientific production is fundamental to determining the opportunities for the future of the biosensing field. This article aims to map scientific production in the biosensors field by running a bibliometric analysis of journal articles registered in the Web of Science database under biosensor-related vital concepts. The key concepts were selected by researchers and biosensor technology developers working on the BioAssembler Horizon project. The findings lead to identifying the scientific and technological knowledge base on biosensing devices and tracking the main scientific organisations developing this technology throughout the COVID-19 period (2019-2023). The institutional origin of the publications characterised the global distribution of related knowledge competencies and research partnerships. These results are discussed, shedding light on the scientific, economic, political, and structural factors that contribute to the formation of a scientific knowledge-based focus on the performance and design of these sensors. Moreover, the lack of scientific ties between the three axes of organisations producing expertise in this area (China, USA, and Russia) points towards the need to find synergies through new mechanisms of co-authorship and collaboration.

Variability-enhanced knowledge-based engineering (VEN) for reconfigurable molds

Variability-enhanced knowledge-based engineering (VEN) for reconfigurable molds

Intelligent templates to prevent geometric parameterization failure in ship superstructure structural detail design

ABSTRACT Ship superstructures, as typical examples of non-standard design elements, require diverse structural geometric designs and frequent adaptations to meet evolving demands and simulation requirements during the detail design process. However, the utilization of current ship computer-aided design (CAD) systems for detail design presents several limitations, and traditional parametric methods often fall short in complex geometric parameterization. This paper proposed intelligent templates via knowledge-based engineering (KBE) to enhance CAD models' robustness and applicability. The study focused on three specific geometric problems that restrict the validity of parameterization: varying types of input conditions, multiple solutions in offsets and arc fillets, and non-topological transformations. Three representative structural component templates were developed on CATIA V6, showcasing effectiveness compared to traditional methods through a ship design task. Furthermore, this approach provides the basis for the implementation of modular design, design automation, and integrated design simulation.

Exploring epistemic aspects of engineering for K–12 science and engineering education

AbstractBackgroundSince the advent of the Next Generation Science Standards (NGSS), there has been an increasing focus on engineering in K–12 education. As educators and researchers have gained a better understanding of the nature of engineering in the decade following the release of the NGSS, there are new opportunities for growth and complexity.PurposeThis study used an epistemological approach to seek insights from experts in the science and engineering communities about the prominent aspects of engineering for K–12 education.Design/MethodThis mixed‐methods study employed a three‐round Delphi study and a focus group meeting to elicit experts' opinions on the epistemic aspects of engineering that could shed light on the nature of engineering knowledge and practices. Constructivist grounded theory methodology was used to identify preliminary themes concerning the epistemic aspects of engineering and then to develop main themes by combining relevant themes together.ResultsThe analysis process yielded 21 preliminary themes that reflect key ideas about the engineering knowledge base, engineering design activities, and values and norms of the engineering community. Additionally, the current study identified eight main themes that captured the broader patterns in the data.ConclusionsThe outcome of this study, including the preliminary themes and main themes concerning the epistemic aspects of engineering, could serve as a conceptual tool for establishing and improving students' conceptions of engineering and as a guide for designing and implementing engineering design activities in K–12 education settings.

How does intellectual property system affect technology entrepreneurship: evidence from Chinese prefecture‐level cities

Technology entrepreneurship plays a pivotal role in bolstering regional economic vitality, with the intellectual property rights (IPR) system serving as a fundamental safeguard for entrepreneurs' intellectual achievements. Using panel data encompassing 260 prefecture‐level cities in China from 2006 to 2019, we employ the count of registered technology‐based enterprises to construct an indicator of technology entrepreneurship and consider China's IPR policy as a quasi‐natural experiment to examine the impact of the IPR system on technology entrepreneurship. The findings reveal that the pilot policy can significantly stimulate technology entrepreneurship displaying a periodically dynamic promotional effect. This conclusion remains reliable even after undergoing rigorous robustness tests. The mechanism analysis demonstrates that the pilot policy can stimulate technology entrepreneurship by enriching the technological knowledge base and providing financial guarantees. Notably, while the policy's impact on technology entrepreneurship increases with the expansion of the city scale across different scenarios, no discernible regional disparities emerge. This study provides a new perspective on the relationship between the IPR system and entrepreneurship, offering valuable insights into improving the IPR framework in emerging developing countries.

Multidisciplinary Automation in Design of Turbine Vane Cooling Channels

In the quest to enhance the efficiency of gas turbines, there is a growing demand for innovative solutions to optimize high-pressure turbine blade cooling. However, the traditional methods for achieving this optimization are known for their complexity and time-consuming nature. We present an automation framework to streamline the design, meshing, and structural analysis of cooling channels, achieving design automation at both the morphological and topological levels. This framework offers a comprehensive approach for evaluating turbine blade lifetime and enabling multidisciplinary design analyses, emphasizing flexibility in turbine cooling design through high-level CAD templates and knowledge-based engineering. The streamlined automation process, supported by a knowledge base, ensures continuity in both the mesh and structural simulation automations, contributing significantly to advancements in gas turbine technology.

Enhanced Design Automation for Hydraulic Manifolds Produced Using Additive Manufacturing

Complex, highly functional components such as hydraulic manifolds can be produced using metal additive manufacturing (AM). The design process is a major challenge in the development of AM components that exploit this potential. Since traditional CAD systems are not made to efficiently create highly complex designs, new approaches to the design process are required. Design Automation (DA) and Knowledge-Based Engineering (KBE) are approaches that can be used to create designs and implement design changes faster and improve functionality. For the application of fluid components, KBE design workflows were developed which enable the automated generation of AM manifolds.However, these workflows have some limitations that need to be improved to provide a flexible design tool that enables users to automatically create geometries that meet all requirements. This paper shows how the workflows have been improved by introducing two new features. First, the object-oriented input format presents a new approach to how the information of a hydraulic circuit diagram can be processed to start the workflows. This is done through a component library that contains a selection of flow components providing all information and geometries of one component, a 3D layout assembly to orient the relevant components in space, and a connectivity matrix that defines the connection through flow channels for the manifold. Second, the bending radius constraint allows users to define a radius that the centerline of a flow channel should not undergo along the whole path. The new features are presented in detail in this paper. Further, the advantages are shown and compared to the previously published workflows in a case study. This work lays the foundation for future work that has the aim to fully automate the design process by only taking a hydraulic circuit diagram as an input and providing all required manufacturing files as an output.

Research on the Modular Design and Application of Prefabricated Components Based on KBE

The design and production of prefabricated buildings pose challenges in achieving standardization, limiting their extensive adoption. In order to address issues of prefabricated components, such as the low reusability of design knowledge, limited standardization, and design disconnection, this paper adopted the prefabricated cantilevered structure components as the research object. It employs knowledge-based engineering (KBE) theory and secondary split modularization approach in conjunction with Revit secondary development technology to establish a modular design system. The system formalizes complex design knowledge into concise user interfaces and a logically clear programming language, ensuring the design system’s ease of use and accessibility. To validate the authenticity and applicability of the modular design system developed in this paper, a comparison is made between the traditional modeling tool and modular modeling tool. Through empirical analysis, the result indicates that the new tool proposed in this paper can enhance the efficiency of design professionals by 72.92%. Among these, the tool meets the modeling and design requirements of 96.1% of the prefabricated components in the project, making it highly suitable for the modeling and design process of the vast majority of prefabricated components. Therefore, this design approach, which integrates KBE and three-dimensional geometric technology, makes the modular design of prefabricated cantilevered structural components feasible, providing a reference for future research in the design of other prefabricated components.

Utilizing Artificial Intelligence and Knowledge-Based Engineering Techniques in Shipbuilding: Practical Insights and Viability

_ This paper delves into the technical aspects and viability of integrating artificial intelligence (AI) and knowledge-based engineering (KBE) tools in practical design. The goal is to digitally embed the hands-on expertise and technical boundaries set by seasoned professionals during intricate engineering and preparatory phases. We showcase how AI/KBE tools might emulate human cognitive processes to make well-informed choices. The article also probes the prospective economic and modernization repercussions of this innovation. Our findings suggest that such an integration is feasible and can amplify the decision-making efficacy and advance the sophistication of CAD/CAM systems in the shipbuilding realm. Furthermore, this investigation underscores the promising future of AI/KBE tools in ship design and advocates for continued exploration and innovation in this sector to fully harness its advantages. Introduction Shipbuilding has long been intertwined with CAD/CAM technologies. As technology evolves, so does the landscape of ship design and manufacturing (Ross, 1950). Traditionally, ship design leaned heavily on seasoned engineers and designers, whose insights were cultivated over years of experience. However, with the rising demand for ships and an aging workforce, there’s a pressing need for enhanced design methodologies. Enter the era of artificial intelligence (AI) and knowledge-based engineering (KBE), which promise to revolutionize ship design by integrating practical knowledge and technical constraints. In today’s shipbuilding scenario, younger engineers often handle detailed engineering stages, a shift from when experienced professionals dominated the shop floor (Moyst and Das, 2005). Our research aims to assess the feasibility of AI KBE systems in enhancing the ship design process during these stages, by virtualizing the knowledge of experienced workers.

Possibility Assessment of Automatic Formation of Technological Processes in Small Batch Production

The work is devoted to urgent problem solution of lack of convenient tools with functional capabilities of the system of route technological process automated design, adapted to small-scale production. The estimation of potential possibility of automatic small-scale production formation by means of estimation of efficiency and expediency technological processes of automation of key stages of design of production technological preparation, such as equipment selection, selection of cutting modes, workpiece, cutting and measuring tools and tooling selection is given. The key stages of technological process development are considered and characterized, such as selection of technological bases, type of workpiece and equipment, formation of route technological process, calculation of cutting modes, selection of allowances and so on. The paper shows both the relevance and necessity of analyzing approaches to automation and their correct adaptation to production needs to achieve maximum efficiency and resource savings for small batch production, and the high relevance for small-batch production of the automatic formation of route technological processes. At the same time, the paper shows the lack of automated systems adapted to the specifics of small-batch production. The existing approaches to the formation of stages are analyzed, as well as the possible methods of automation of stages and the existing limitations of these methods are considered, the efficiency of the considered approaches is evaluated. The methods of automation of separate stages of technological preparation of production are analyzed and the efficiency of the considered approaches is evaluated. The original concept of solving the problem of automation for small-scale production by using a combination of a limited number of standard solutions, represented as a set of technological parameters or knowledge base, with individual solutions, adapted to the specifics of enterprises and industry, is proposed and the potential effectiveness of this approach is shown.

Organelle-dependent polyprotein designs enable stoichiometric expression of nitrogen fixation components targeted to mitochondria

Introducing nitrogen fixation (nif ) genes into eukaryotic genomes and targeting Nif components to mitochondria or chloroplasts is a promising strategy for engineering nitrogen-fixing plants. A prerequisite for achieving nitrogen fixation in crops is stable and stoichiometric expression of each component in organelles. Previously, we designed a polyprotein-based nitrogenase system depending on Tobacco Etch Virus protease (TEVp) to release functional Nif components from five polyproteins. Although this system satisfies the demand for specific expression ratios of Nif components in Escherichia coli, we encountered issues with TEVp cleavage of polyproteins targeted to yeast mitochondria. To overcome this obstacle, a version of the Nif polyprotein system was constructed by replacing TEVp cleavage sites with minimal peptide sequences, identified by knowledge-based engineering, that are susceptible to cleavage by the endogenous mitochondrial-processing peptidase. This replacement not only further reduces the number of genes required, but also prevents potential precleavage of polyproteins outside the target organelle. This version of the polyprotein-based nitrogenase system achieved levels of nitrogenase activity in E. coli, comparable to those observed with the TEVp-based polyprotein nitrogenase system. When applied to yeast mitochondria, stable and balanced expression of Nif components was realized. This strategy has potential advantages, not only for transferring nitrogen fixation to eukaryotic cells, but also for the engineering of other metabolic pathways that require mitochondrial compartmentalization.

The Implementation of 3D Printing in Engineering Education in the Field of Hydraulic and Pneumatic Components.

The implementation of three-dimensional (3D) printing for education in different fields brings numerous advantages that make learning easier for students. Also, it changes the model of students' participation in the teaching process so that they become active participants. This article considers the involvement of 3D printing in the teaching process in the field of hydraulic and pneumatic components by using the developed methodology. The goal was to find an approach that would help students to easier understand the design and operation principles of these components. The methodology was developed and applied in various forms of the teaching process during a 5-year period. The application of the methodology was evaluated based on the results of the online questionnaire. Traditional theoretical approaches enable a good transfer of basic engineering knowledge, but students are usually not ready to apply them in practice. The results show that the largest number of students (81%) stated that 3D printing should be included in the teaching process. The main reason was a better understanding of the considered components and the possibility of the application of acquired knowledge in the industry. 3D printing enables students to understand the considered component, its design, and operational principle. Further application of 3D printing enables the development of additional skills related to the developing, modeling, and manufacturing (printing) components. Also, students expressed increased motivation for learning.

Industry 4.0 Technological Advancement in the Food and Beverage Manufacturing Industry in South Africa—Bibliometric Analysis via Natural Language Processing

The food and beverage (FOODBEV) manufacturing industry is a significant contributor to global economic development, but it is also subject to major global competition. Manufacturing technology evolution is rapid and, with the Fourth Industrial Revolution (4IR), ever accelerating. Thus, the ability of companies to review and identify appropriate, beneficial technologies and forecast the skills required is a challenge. 4IR technologies, as a collection of tools to assist technological advancement in the manufacturing sector, are essential. The vast and diverse global technology knowledge base, together with the complexities associated with screening in technologies and the lack of appropriate enablement skills, makes technology selection and implementation a challenge. This challenge is premised on the knowledge that there are vast amounts of information available on various research databases and web search engines; however, the extraction of specific and relevant information is time-intensive. Whilst existing techniques such as conventional bibliometric analysis are available, there is a need for dynamic approaches that optimise the ability to acquire the relevant information or knowledge within a short period with minimum effort. This research study adopts smart knowledge management together with artificial intelligence (AI) for knowledge extraction, classification, and adoption. This research defines 18 FOODBEV manufacturing processes and adopts a two-tier Natural Language Processing (NLP) protocol to identify technological substitution for process optimisation and the associated skills required in the FOODBEV manufacturing sector in South Africa.

BamnetTL: Bidirectional Attention Memory Network with Transfer Learning for Question Answering Matching

In KBQA (knowledge base question answering), questions are processed using NLP (natural language processing), and knowledge base technology is used to generate the corresponding answers. KBQA is one of the most challenging tasks in the field of NLP. Q&A (question and answer) matching is an important part of knowledge base QA (question answering), in which the correct answer is selected from candidate answers. At present, Q&A matching task faces the problem of lacking training data in new fields, which leads to poor performance and low efficiency of the question answering system. The paper puts forward a KBQA Q&A matching model for deep feature transfer based on a bidirectional attention memory network, BamnetTL. It uses biattention to collect information from the knowledge base and question sentences in both directions in order to improve the accuracy of Q&A matching and transfers knowledge from different fields through a deep dynamic adaptation network. BamnetTL improves the accuracy of Q&A matching in the target domain by transferring the knowledge in the source domain with more training resources to the target domain with fewer training resources. The experimental results show that the proposed method is effective.