Search results for: Process models

Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

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Authors: Maria Zinnia Bardas Hoffmann

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Contrastive Analysis (CA) is the systematic comparison between two languages. It stems from the notion that errors are caused by interference of the L1 system in the acquisition process of an L2. CA represents a useful tool to understand the nature of learning and acquisition. Also, this particular method promises a path to un-derstand the nature of underlying cognitive processes, even when other factors such as intrinsic motivation and teaching strategies were found to best explain student’s problems in acquisition. CA study is justified not only from the need to get a deeper understanding of the nature of SLA, but as an invaluable source to provide clues, at a cognitive level, for those general processes involved in rule formation and abstract thought. It is relevant for cross disciplinary studies and the fields of Computational Thought, Natural Language processing, Applied Linguistics, Cognitive Linguistics and Math Theory. That being said, this paper intends to address here as well its own set of constraints and limitations. Finally, this paper: (a) aims at identifying some of the difficulties students may find in their learning process due to the nature of their specific variety of L1, Rio de la Plata Spanish (RPS), (b) represents an attempt to discuss the necessity for specific models to approach CA.

Keywords: second language acquisition, applied linguistics, contrastive analysis, applied contrastive analysis English language department, meta-linguistic rules, cross-linguistics studies, computational thought, natural language processing

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Authors: Laleh Taheri, Noraini C. Pa, Rusli Abdullah, Salfarina Abdullah

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Knowledge plays an important role to the success of any organization. Software development organizations are highly knowledge-intensive organizations especially in their Requirement Elicitation Process (REP). There are several problems regarding communicating and using the knowledge in REP such as misunderstanding, being out of scope, conflicting information and changes of requirements. All of these problems occurred in transmitting the requirements knowledge during REP. Several researches have been done in REP in order to solve the problem towards requirements. Knowledge Audit (KA) approaches were proposed in order to solve managing knowledge in human resources, financial, and manufacturing. There is lack of study applying the KA in requirements elicitation process. Therefore, this paper proposes a KA model for REP in supporting to acquire good requirements.

Keywords: knowledge audit, requirement elicitation process, KA model, knowledge in requirement elicitation

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Authors: Ana L. Martins, Luis Orfão

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Healthcare systems have been subject to various research efforts aiming at process improvement under a lean approach. Another perspective, agility, has also been used, though in a lower scale, in order to analyse the ability of different hospital services to adapt to demand uncertainties. Both perspectives have a common denominator, the improvement of effectiveness and efficiency of the services in a healthcare setting context. Mixing the two approached allows, on one hand, to streamline the processes, and on the other hand the required flexibility to deal with demand uncertainty in terms of both volume and variety. The present research aims to analyse the impacts of the combination of both perspectives in the effectiveness and efficiency of an hospital service. The adopted methodology is based on a case study approach applied to the process of the ambulatory surgery service of Hospital de Lamego. Data was collected from direct observations, formal interviews and informal conversations. The analyzed process was selected according to three criteria: relevance of the process to the hospital, presence of human resources, and presence of waste. The customer of the process was identified as well as his perception of value. The process was mapped using flow chart, on a process modeling perspective, as well as through the use of Value Stream Mapping (VSM) and Process Activity Mapping. The Spaghetti Diagram was also used to assess flow intensity. The use of the lean tools enabled the identification of three main types of waste: movement, resource inefficiencies and process inefficiencies. From the use of the lean tools improvement suggestions were produced. The results point out that leagility cannot be applied to the process, but the application of lean and agility in specific areas of the process would bring benefits in both efficiency and effectiveness, and contribute to value creation if improvements are introduced in hospital’s human resources and facilities management.

Keywords: case study, healthcare systems, leagility, lean management

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Authors: Sara El Mansouria Beghdadi

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Quantitative structure–activity relationship (QSAR) modelling is one of the main computer tools used in medicinal chemistry. Over the past two decades, the incidence of fungal infections has increased due to the development of resistance. In this study, the QSAR was performed on a series of esters of 2-carboxamido-3-(1H-imidazole-1-yl) propanoic acid derivatives. These compounds have showed moderate and very good antifungal activity. The multiple linear regression (MLR) was used to generate the linear 2d-QSAR models. The dataset consists of 115 compounds with their antifungal activity (log MIC) against «Candida albicans» (ATCC SC5314). Descriptors were calculated, and different models were generated using Chemoffice, Avogadro, GaussView software. The selected model was validated. The study suggests that the increase in lipophilicity and the reduction in the electronic character of the substituent in R1, as well as the reduction in the steric hindrance of the substituent in R2 and its aromatic character, supporting the potentiation of the antifungal effect. The results of QSAR could help scientists to propose new compounds with higher antifungal activities intended for immunocompromised patients susceptible to multi-resistant nosocomial infections.

Keywords: quantitative structure–activity relationship, imidazole, antifungal, candida albicans (ATCC SC5314)

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Authors: Migael van Zyl, Stefanie Visser, Awelani Phaswana

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The electricity landscape in South Africa is characterized by frequent occurrences of loadshedding, a measure implemented by Eskom to manage electricity generation shortages by curtailing demand. Loadshedding, classified into stages ranging from 1 to 8 based on severity, involves the systematic rotation of power cuts across municipalities according to predefined schedules. However, this practice introduces distortions in recorded electricity demand, posing challenges to accurate forecasting essential for budgeting, network planning, and generation scheduling. Addressing this challenge requires the development of a methodology to quantify the impact of loadshedding and integrate it back into metered electricity demand data. Fortunately, comprehensive records of loadshedding impacts are maintained in a database, enabling the alignment of Loadshedding effects with hourly demand data. This adjustment ensures that forecasts accurately reflect true demand patterns, independent of loadshedding's influence, thereby enhancing the reliability of electricity supply management in South Africa. This paper presents a methodology for determining the hourly impact of load scheduling and subsequently adjusting historical demand data to account for it. Furthermore, two forecasting models are developed: one utilizing the original dataset and the other using the adjusted data. A comparative analysis is conducted to evaluate forecast accuracy improvements resulting from the adjustment process. By implementing this methodology, stakeholders can make more informed decisions regarding electricity infrastructure investments, resource allocation, and operational planning, contributing to the overall stability and efficiency of South Africa's electricity supply system.

Keywords: electricity demand forecasting, load shedding, demand side management, data science

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Authors: Snigdha Sahai, Devaki Chikkavenkatappa Yellappa

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This study outlines approaches for testing and validating stochastic models used in epidemiology, focusing on the integration and functional testing of simulation code. It details methods for combining simple functions into comprehensive simulations, distinguishing between deterministic and stochastic components, and applying tests to ensure robustness. Techniques include isolating stochastic elements, utilizing large sample sizes for validation, and handling special cases. Practical examples are provided using R code to demonstrate integration testing, handling of incorrect inputs, and special cases. The study emphasizes the importance of both functional and defensive programming to enhance code reliability and user-friendliness.

Keywords: computational epidemiology, epidemiology, public health, infectious disease modeling, statistical analysis, health data analysis, disease transmission dynamics, predictive modeling in health, population health modeling, quantitative public health, random sampling simulations, randomized numerical analysis, simulation-based analysis, variance-based simulations, algorithmic disease simulation, computational public health strategies, epidemiological surveillance, disease pattern analysis, epidemic risk assessment, population-based health strategies, preventive healthcare models, infection dynamics in populations, contagion spread prediction models, survival analysis techniques, epidemiological data mining, host-pathogen interaction models, risk assessment algorithms for disease spread, decision-support systems in epidemiology, macro-level health impact simulations, socioeconomic determinants in disease spread, data-driven decision making in public health, quantitative impact assessment of health policies, biostatistical methods in population health, probability-driven health outcome predictions

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Authors: M. Gil, R. Montalvo

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Business Intelligence (BI) has become a major source of competitive advantages for firms around the world. BI has been defined as the process of data visualization and reporting for understanding what happened and what is happening. Moreover, BI has been studied for its predictive capabilities in the context of trade and financial transactions. The current literature has identified that BI permits managers to identify market trends, understand customer relations, and predict demand for their products and services. This last capability of BI has been of special concern to academics. Specifically, due to its power to build predictive models adaptable to specific time horizons and geographical regions. However, the current literature of BI focuses on predicting specific markets and industries because the impact of such predictive models was relevant to specific industries or organizations. Currently, the existing literature has not developed a predictive model of BI that takes into consideration the whole economy of a geographical area. This paper seeks to create a predictive model of BI that would show the bigger picture of a geographical area. This paper uses a data set from the Secretary of Economic Development of the state of Jalisco, Mexico. Such data set includes data from all the commercial transactions that occurred in the state in the last years. By analyzing such data set, it will be possible to generate a BI model that predicts supply and demand from specific industries around the state of Jalisco. This research has at least three contributions. Firstly, a methodological contribution to the BI literature by generating the predictive supply and demand model. Secondly, a theoretical contribution to BI current understanding. The model presented in this paper incorporates the whole picture of the economic field instead of focusing on a specific industry. Lastly, a practical contribution might be relevant to local governments that seek to improve their economic performance by implementing BI in their policy planning.

Keywords: business intelligence, predictive model, supply and demand, Mexico

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Authors: Hiba Mezaache

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The current study aimed to know the impact of using information and communication technology on the process of re-engineering small and medium enterprises, as the world witnessed the speed development of the latter in its field of work and the diversity of its objectives and programs, that also made its process important for the growth and development of the institution and also gaining the flexibility to face the changes that may occur in the environment of work, so in order to know the impact of information and communication technology on the success of this process, we prepared an electronic questionnaire that included (70) items, and we also used the SPSS statistical calendar to analyze the data obtained. In the end of our study, our conclusion was that there was a positive correlation between the four dimensions of information and communication technology, i.e., hardware and equipment, software, communication networks, databases, and the re-engineering process, in addition to the fact that the studied institutions attach great importance to formal communication, for its positive advantages that it achieves in reducing time and effort and costs in performing the business. We could also say that communication technology contributes to the process of formulating objectives related to the re-engineering strategy. Finally, we recommend the necessity of empowering workers to use information technology and communication more in enterprises, and to integrate them more into the activity of the enterprise by involving them in the decision-making process, and also to keep pace with the development in the field of software, hardware, and technological equipment.

Keywords: information and communication technology, re-engineering, small and medium enterprises, the impact

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Authors: Petr Šimek, Kamil Kostruk

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This paper deals with the problem of testing the Electronic Control Unit (ECU) for the specified function validation. Modern ECUs have many functions which need to be tested. This process requires tracking between the test and the specification. The technique discussed in this paper explores the system for automating this process. The paper focuses in its chapter IV on the introduction to the problem in general, then it describes the proposed test system concept and its principle. It looks at how the process of the ECU interface specification file for automated interface testing and test tracking works. In the end, the future possible development of the project is discussed.

Keywords: electronic control unit testing, embedded system, test generate, test automation, process automation, CAN bus, ethernet

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Authors: Maria C. Mariani, Md Al Masum Bhuiyan, Osei K. Tweneboah, Hector G. Huizar

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This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties.

Keywords: Augmented Dickey Fuller Test, geophysical time series, maximum likelihood estimation, stochastic volatility model

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Authors: Tohid Rahimi, Yahya Naderi, Babak Yousefi, Seyed Hossein Hoseini

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Today’s modern interconnected power system is highly complex in nature. In this, one of the most important requirements during the operation of the electric power system is the reliability and security. Power and frequency oscillation damping mechanism improve the reliability. Because of power system stabilizer (PSS) low speed response against of major fault such as three phase short circuit, FACTs devise that can control the network condition in very fast time, are becoming popular. However, FACTs capability can be seen in a major fault present when nonlinear models of FACTs devise and power system equipment are applied. To realize this aim, the model of multi-machine power system with FACTs controller is developed in MATLAB/SIMULINK using Sim Power System (SPS) blockiest. Among the FACTs device, Static synchronous series compensator (SSSC) due to high speed changes its reactance characteristic inductive to capacitive, is effective power flow controller. Tuning process of controller parameter can be performed using different method. However, Genetic Algorithm (GA) ability tends to use it in controller parameter tuning process. In this paper, firstly POD controller is used to power oscillation damping. But in this station, frequency oscillation dos not has proper damping situation. Therefore, FOD controller that is tuned using GA is using that cause to damp out frequency oscillation properly and power oscillation damping has suitable situation.

Keywords: power oscillation damping (POD), frequency oscillation damping (FOD), Static synchronous series compensator (SSSC), Genetic Algorithm (GA)

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Authors: Babak Mohajeri

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In recent years, the conventional business model of ownership has been changed towards accessibility in a variety of markets. Two trends can be observed in the evolution of this rental-like business model. Firstly, the technological development that enables the emergence of new business models. These new business models increasingly become agile and flexible. For example Spotify, an online music stream company provides consumers access to over millions of music tracks, conveniently through the smartphone, tablet or computer. Similarly, Car2Go, the car sharing company accesses its members with flexible and nearby sharing cars. The second trend is the increasing communication and connections via social networks. This trend enables a shift to peer-to-peer accessibility based business models. Conventionally, companies provide access for their customers to own companies products or services. In peer-to-peer model, nonetheless, companies facilitate access and connection across their customers to use other customers owned property or skills, competencies or services .The is so-called the sharing economy business model. The aim of this study is to investigate into a new and emerging type of the sharing economy model in which role of customers and service providers may dramatically change. This new model is called Collaborative Service Networks. We propose a mechanism for Collaborative Service Networks business model. Uber and Airbnb, two successful growing companies, have been selected for our case studies and their business models are analyzed. Finally, we study the emergence of the collaborative service networks in the manufacturing domain. Our finding results to a new manufacturing paradigm called social manufacturing.

Keywords: sharing economy, collaborative service networks, social manufacturing, manufacturing development

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Authors: Vinayak Malaghan, Digvijay Pawar

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Speed is the independent parameter which plays a vital role in the highway design. Design consistency of the highways is checked based on the variation in the operating speed. Often the design consistency fails to meet the driver’s expectation which results in the difference between operating and design speed. Literature reviews have shown that significant crashes take place in horizontal curves due to lack of design consistency. The paper focuses on continuous speed profile study on tangent to curve transition for both day and night daytime. Data is collected using GPS device which gives continuous speed profile and other parameters such as acceleration, deceleration were analyzed along with Tangent to Curve Transition. In this present study, models were developed to predict operating speed on tangents and horizontal curves as well as model indicating the speed reduction from tangent to curve based on continuous speed profile data. It is observed from the study that vehicle tends to decelerate from approach tangent to between beginning of the curve and midpoint of the curve and then accelerates from curve to tangent transition. The models generated were compared for both day and night and can be used in the road safety improvement by evaluating the geometric design consistency.

Keywords: operating speed, design consistency, continuous speed profile data, day and night time

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Authors: Daniel Zarzo Montes, Manuel Zarzo Castelló

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Proteins play a fundamental role in biology, but their structure is complex, and it is a challenge for teachers to conceptually explain the differences between their primary, secondary, tertiary, and quaternary structures. On the other hand, there are currently many computer programs to visualize the 3D structure of proteins, but they require advanced training and knowledge. Moreover, it becomes difficult to visualize the sequence of amino acids in these models, and how the protein conformation is reached. Given this drawback, a simple and instructive procedure is proposed in order to teach the protein structure to undergraduate and graduate students. For this purpose, insulin has been chosen because it is a protein that consists of 51 amino acids, a relatively small number. The methodology has consisted of the use of plastic atom models, which are frequently used in organic chemistry and biochemistry to explain the chirality of biomolecules. For didactic purposes, when the aim is to teach the biochemical foundations of proteins, a manipulative system seems convenient, starting from the chemical structure of amino acids. It has the advantage that the bonds between amino acids can be conveniently rotated, following the pattern marked by the 3D models. First, the 51 amino acids were modeled, and then they were linked according to the sequence of this protein. Next, the three disulfide bonds that characterize the stability of insulin have been established, and then the alpha-helix structure has been formed. In order to reach the tertiary 3D conformation of this protein, different interactive models available on the Internet have been visualized. In conclusion, the proposed methodology seems very suitable for biology and biochemistry students because they can learn the fundamentals of protein modeling by means of a manipulative procedure as a basis for understanding the functionality of proteins. This methodology would be conveniently useful for a biology or biochemistry laboratory practice, either at the pre-graduate or university level.

Keywords: protein structure, 3D model, insulin, biomolecule

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Authors: D. Pérez, M. M. E. Alemany

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In this work a Framework to model the Supply Chain (SC) Collaborative Planning (CP) Process is proposed, and particularly its Decisional view. The main Framework contributions with regards to previous related works are the following, 1) the consideration of not only the Decision view, the most important one due to the Process type, but other additional three views which are the Physical, Organisation and Information ones, closely related and complementing the Decision View, 2) the joint consideration of two interdependence types, the Temporal (among Decision Centres belonging to different Decision Levels) and Spatial (among Decision Centres belonging to the same Decision Level) to support the distributed Decision-Making process in SC where several decision Centres interact among them in a collaborative manner.

Keywords: collaborative planning, decision view, distributed decision-making, framework

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Authors: H. Al-Hajeri

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The computation of the effectiveness of turbulence enhancement surface features, such as ribs as means of promoting mixing and hence heat transfer, has attracted the continued attention of the engineering community. In this study, the simulation of a three-dimensional cooling passage is carried out employing a number of turbulence models including Durbin model. The cooling passage consists of a square section duct whose upper and lower surfaces feature staggered cuboid ribs. The main objective of this paper is to provide comparisons of the performance of the v2-f model against other established turbulence models as implemented in the commercial CFD code Ansys Fluent. The present study demonstrates that the v2-f model can successfully capture the isothermal air flow phenomena in flow over obstacles.

Keywords: CFD, cooling passage, Durbin model, turbulence model

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Authors: J. Y. Heo, H. G. Sung

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Numerical simulations have been performed for assessment of compressibility correction of two-equation turbulence models suitable for large scale separation flows perturbed by pintle strokes. In order to take into account pintle movement, a sliding mesh method was applied. The chamber pressure, mass flow rate, and thrust have been analyzed, and the response lag and sensitivity at the chamber and nozzle were estimated for a movable pintle. The nozzle performance for pintle reciprocating as its insertion and extraction processes, were analyzed to better understand the dynamic performance of the pintle nozzle.

Keywords: pintle, sliding mesh, turbulent model, compressibility correction

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Authors: Nabil Shawkat, Jamil Saquer

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Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

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Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

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Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

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Authors: Younis Elhaddad, Alfonso Ortega

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Oil production by means of gas lift is a standard technique in oil production industry. To optimize the total amount of oil production in terms of the amount of gas injected is a key question in this domain. Different methods have been tested to propose a general methodology. Many of them apply well-known numerical methods. Some of them have taken into account the power of evolutionary approaches. Our goal is to provide the experts of the domain with a powerful automatic searching engine into which they can introduce their knowledge in a format close to the one used in their domain, and get solutions comprehensible in the same terms, as well. These proposals introduced in the genetic engine the most expressive formal models to represent the solutions to the problem. These algorithms have proven to be as effective as other genetic systems but more flexible and comfortable for the researcher although they usually require huge search spaces to justify their use due to the computational resources involved in the formal models. The first step to evaluate the viability of applying our approaches to this realm is to fully understand the domain and to select an instance of the problem (gas lift optimization) in which applying genetic approaches could seem promising. After analyzing the state of the art of this topic, we have decided to choose a previous work from the literature that faces the problem by means of numerical methods. This contribution includes details enough to be reproduced and complete data to be carefully analyzed. We have designed a classical, simple genetic algorithm just to try to get the same results and to understand the problem in depth. We could easily incorporate the well mathematical model, and the well data used by the authors and easily translate their mathematical model, to be numerically optimized, into a proper fitness function. We have analyzed the 100 curves they use in their experiment, similar results were observed, in addition, our system has automatically inferred an optimum total amount of injected gas for the field compatible with the addition of the optimum gas injected in each well by them. We have identified several constraints that could be interesting to incorporate to the optimization process but that could be difficult to numerically express. It could be interesting to automatically propose other mathematical models to fit both, individual well curves and also the behaviour of the complete field. All these facts and conclusions justify continuing exploring the viability of applying the approaches more sophisticated previously proposed by our research group.

Keywords: evolutionary automatic programming, gas lift, genetic algorithms, oil production

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Authors: Areej R. Banjar, Abraham G. Campbell

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The use of Mixed Reality (MR) in teaching and training is growing popular and can improve students’ ability to perform technical procedures. This short paper outlines the creation of an interactive educational MR 3D application that aims to improve the quality of instruction for dentistry students. This application is called MRImplantology and aims to teach the fundamentals and preoperative planning of dental implant placement. MRImplantology uses cone-beam computed tomography (CBCT) images as the source for 3D dental models that dentistry students will be able to freely manipulate within a 3D MR world to aid their learning process.

Keywords: augmented reality, education, dentistry, cone-beam computed tomography CBCT, head mounted display HMD, mixed reality

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Authors: Widjojo Suprapto, Zeplin Jiwa Husada Tarigan, Sautma Ronni Basana

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Business process reengineering can be conducted by some procedure rationalization for all related departments in a company so that all data and business processes are connected. The changing of any business process is used to set up the working standard so that it gives an impact to the implementation of ERP and the company performance. After collecting and processing the data from 77 manufacturing companies, it is obtained that BPR (Business Process Reengineering) has no direct impact on the implementation of ERP (Enterprise Resource Planning) in the companies and manufacturing performance; however, it influences the implementation of TQM. The implementation of TQM influences directly the implementation of ERP, but it does not influence directly the company performance. The implementation of ERP gives a significant increase in the work performance of the manufacturing companies in East Java.

Keywords: enterprise resources planning, business process reengineering, TQM, company performance

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Authors: Prasenjit Singha, Ajay Kumar Shukla

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To produce high-quality steel in larger volumes, dynamic control of composition and temperature throughout the process is essential. In this paper, we developed a mass transfer model based on thermodynamics to simulate the ladle metallurgy steel-making process using FactSage and its macro facility. The overall heat and mass transfer processes consist of one equilibrium chamber, two non-equilibrium chambers, and one adiabatic reactor. The flow of material, as well as heat transfer, occurs across four interconnected unit chambers and a reactor. We used the macro programming facility of FactSage™ software to understand the thermochemical model of the secondary steel making process. In our model, we varied the oxygen content during the process and studied their effect on the composition of the final hot metal and slag. The model has been validated with respect to the plant data for the steel composition, which is similar to the ladle metallurgy steel-making process in the industry. The resulting composition profile serves as a guiding tool to optimize the process of ladle metallurgy in steel-making industries.

Keywords: desulphurization, degassing, factsage, reactor

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Authors: F. Villaret, I. Jacot, Y. Shen, Z. Kong, T. XU, Y. Wang, D. Lu

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Wire Arc Additive Manufacturing (WAAM) allows the rapid production of large, homogeneous parts with complex geometry. However, in the nuclear field, parts can reach dimensions of ten to a hundred tons. In this case, the usual WAAM TIG or CMT processes do not have sufficient deposition rates to consider the manufacture of parts of such dimensions within a reasonable time. The submerged arc welding process (SAW, Submerged Arc Welding) allows much higher deposition rates. Although there are very few references to this process for additive manufacturing in the literature, it has been used for a long time for the welding and coating of nuclear power plant vessels, so this process is well-known and mastered as a welding process. This study proposes to evaluate the SAW process as an additive manufacturing technique by taking as an example a low-alloy steel of type 16MND5. In the first step, a parametric study allowed the evaluation of the effect of the different parameters and the deposition rate on the geometry of the beads and their microstructure. Larger parts were also fabricated and characterized by metallography and mechanical tests (tensile, impact, toughness). The effect of different heat treatments on the microstructure is also studied.

Keywords: WAAM, low alloy steel, submerged arc, caracterization

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Authors: Wasif Mughees, Malik Al-Ahmad

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This research presents the graphical design analysis and mathematical programming technique to dig out the possible water allocation distribution to minimize water usage in process units. The study involves the mass and property integration in its core methodology. Tehran Oil Refinery is studied to implement the focused water pinch technology for regeneration, reuse and recycling of water streams. Process data is manipulated in terms of sources and sinks, which are given in terms of properties. Sources are the streams to be allocated. Sinks are the units which can accept the sources. Suspended Solids (SS) is taken as a single contaminant. The model minimizes the mount of freshwater from 340 to 275m3/h (19.1%). Redesigning and allocation of water streams was built. The graphical technique and mathematical programming shows the consistency of results which confirms mass transfer dependency of water streams.

Keywords: minimization, water pinch, process integration, pollution prevention

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Authors: Ramin Vafadary, Maryam Khanbaghi

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Forecasting electricity load is important for various purposes like planning, operation, and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet, and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria, namely, the mean absolute error and root mean square error. The National Renewable Energy Laboratory (NREL) residential energy consumption data is used to train the models. The results of this study show that the SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts, we can improve the robustness of the models for 24 hours ahead of electricity load forecasting.

Keywords: bagging, Fbprophet, Holt-Winters, LSTM, load forecast, SARIMA, TensorFlow probability, time series

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Authors: L. Cesari, L. Canabady-Rochelle, F. Mutelet

Abstract:

The thermal conversion of lignin produces bio-oils that contain many compounds with high added-value such as phenolic compounds. In order to efficiently extract these compounds, the possible use of choline bis(trifluoromethylsulfonyl)imide [Choline][NTf2] ionic liquid was explored. To this end, a multistep approach was implemented. First, binary (phenolic compound and solvent) and ternary (phenolic compound and solvent and ionic liquid) solutions were investigated. Eight binary systems of phenolic compound and water were investigated at atmospheric pressure. These systems were quantified using the turbidity method and UV-spectroscopy. Ternary systems (phenolic compound and water and [Choline][NTf2]) were investigated at room temperature and atmospheric pressure. After stirring, the solutions were let to settle down, and a sample of each phase was collected. The analysis of the phases was performed using gas chromatography with an internal standard. These results were used to quantify the values of the interaction parameters of thermodynamic models. Then, extractions were performed on synthetic solutions to determine the influence of several operating conditions (temperature, kinetics, amount of [Choline][NTf2]). With this knowledge, it has been possible to design and simulate an extraction process composed of one extraction column and one flash. Finally, the extraction efficiency of [Choline][NTf2] was quantified with real bio-oils from lignin pyrolysis. Qualitative and quantitative analysis were performed using gas chromatographic connected to mass spectroscopy and flame ionization detector. The experimental measurements show that the extraction of phenolic compounds is efficient at room temperature, quick and does not require a high amount of [Choline][NTf2]. Moreover, the simulations of the extraction process demonstrate that [Choline][NTf2] process requires less energy than an organic one. Finally, the efficiency of [Choline][NTf2] was confirmed in real situations with the experiments on lignin pyrolysis bio-oils.

Keywords: bio-oils, extraction, lignin, phenolic compounds

Procedia PDF Downloads 110

Authors: Elisabeth Brandenburg, Robert Woll, Rainer Stark

Abstract:

This paper presents how smart glasses and voice commands can be used for improving the maintenance process of industrial gas turbines. It presents the process of inspecting a gas turbine’s combustion chamber and how it is currently performed using a set of paper-based documents. In order to improve this process, a portable hands-free process assistance system has been conceived. In the following, it will be presented how the approach of user-centered design and the method of paper prototyping have been successfully applied in order to design a user interface and a corresponding workflow model that describes the possible interaction patterns between the user and the interface. The presented evaluation of these results suggests that the assistance system could help the user by rendering multiple manual activities obsolete, thus allowing him to work hands-free and to save time for generating protocols.

Keywords: paper prototyping, smart glasses, turbine maintenance, user centered design

Procedia PDF Downloads 321

Authors: Victoria Goodall, Paul Fatti, Norman Owen-Smith

Abstract:

Hidden Markov Models have become popular for the analysis of animal tracking data. These models are being used to model the movements of a variety of species in many areas around the world. A common assumption of the model is that the observations need to have regular time steps. In many ecological studies, this will not be the case. The objective of the research is to modify the movement model to allow for irregularly spaced locations and investigate the effect on the inferences which can be made about the latent states. A modification of the likelihood function to allow for these irregular spaced locations is investigated, without using interpolation or averaging the movement rate. The suitability of the modification is investigated using GPS tracking data for lion (Panthera leo) in South Africa, with many observations obtained during the night, and few observations during the day. Many nocturnal predator tracking studies are set up in this way, to obtain many locations at night when the animal is most active and is difficult to observe. Few observations are obtained during the day, when the animal is expected to rest and is potentially easier to observe. Modifying the likelihood function allows the popular Hidden Markov Model framework to be used to model these irregular spaced locations, making use of all the observed data.

Keywords: hidden Markov Models, irregular observations, animal movement modelling, nocturnal predator

Procedia PDF Downloads 244