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Observational data-driven modeling and optimization of manufacturing processes

EXPERT SYSTEMS WITH APPLICATIONS 2018年 93卷 456-464页

作者： Sadati, Najibesadat Chinnam, Ratna Babu Nezhad, Milad Zafar Wayne State Univ Dept Ind & Syst Engn 4815 Fourth St Detroit MI 48202 USA

The dramatic increase of observational data across industries provides unparalleled opportunities for data-driven decision making and management, including the manufacturing industry. In the context of production, data-driven approaches can exploit observational data to model, control and improve process performance. When supplied by observational data with adequate coverage to inform the true process performance dynamics, they can overcome the cost associated with intrusive controlled designed experiments and can be applied for both process monitoring and improvement. We propose a novel integrated approach that uses observational data for identifying significant control variables while simultaneously facilitating process parameter design. We evaluate our method using data from synthetic experiments and also apply it to a real-world case setting from a tire manufacturing company. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Parameter design Observational data Variable selection data-driven modeling Response surface method Meta-heuristic optimization

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A physically-supported data-driven proxy modeling based on machine learning classification methods: Application to water front movement prediction

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 196卷 107828-107828页

作者： Haghshenas, Yousof Niri, Mohammad Emami Amini, Shahram Kolajoobi, Rasool Amiri Univ Tehran Coll Engn Inst Petr Engn Tehran Iran Petropars Co Tehran Iran

By defining an optimum injection/production strategy in the water flooding process, the water front movement is controlled and an early breakthrough is avoided, and as a result, the sweep efficiency is increased. The most important part of injection/production planning is determining the injection fluid front position. However, using a commercial reservoir simulator comes up with a considerable time and CPU effort, particularly in the case of large and complex reservoirs. Several proxy models, either physics-based or data-driven, have been developed to predict water front movement with low computational time and cost, each offering some advantages and shortcomings such as error accumulation and short-term predictions. In this paper, we use a hybrid view in developing a classification based smart proxy model at the grid-block level (CSPMG) for front prediction to benefit from the advantages of both physics-based and data-driven proxies. The idea is to formulate the problem by using the physical principles underlying the problem (physical-view) and then use machine learning classification models to capture the pattern between inputs and the target feature (data-driven view). Based on the Buckley-Leverett theory, which is a physics-based method for front advancement in porous media, water front prediction was formulated as a classification problem in which the grid-blocks behind and ahead of the front were considered as separate classes and a label was assigned to each class. Then, artificial neural networks (ANNs) were trained on a training database to predict the class label of each grid block. The water front was considered as the boundary between two adjacent classes. A binary and a ternary classification problem were formulated and two proxy models were developed. A blind test was carried out to compare their results with each other, with a data-driven regression model, and with those of a reservoir simulator. The results showed that the CSPMG matches the reservoi

关键词： data-driven modeling Machine learning classification methods Water flooding Artificial intelligence data mining

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data-driven Process modeling and Optimization Aided by Material and Energy Balances: The Case of a Batch Polymerization Process

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IFAC-PapersOnLine 2021年第3期54卷 1-6页

作者： Ahmed Bardooli Yachao Dong Christos Georgakis Chemical and Biological Engineering Department Tufts University Medford MA 02155 USA

Our group recently defined two novel data-driven modeling methodologies: The Design of Dynamic Experiments (DoDE) and the Dynamic Response Surface Methodology (DRSM). These two methods enable the quick and efficient data-driven modeling of processes with a partial understanding of their inner workings. They generalize the Design of Experiments (DoE) and the Response Surfaces Methodology (RSM). DoDE allows time-varying inputs, and DRSM models time-varying process outputs. In this paper, we combine the above data-driven tools and partial knowledge of a batch polymerization process to develop an integrated data and knowledge-driven model. The optimization objective is to minimize the process’s batch time while producing the same product quality, increasing productivity. The process knowledge incorporated into the model consists of material and energy balances in which we lack a quantitative description of the rate phenomena, such as reaction or mass/heat transfer rates. The optimization is evolutionary; initially, targeting small improvements through constrained extrapolations around the normal operating conditions. Then, we build the first models and use such models to design the next set of experiments that meet our specifications. This cycle of running experiments and updating the models is repeated until an optimum is reached. After three cycles, we succeeded in reducing the batch time by 26%, while producing acceptable product.

关键词： Industrial Applications of Process Control Batch Semi-batch Process Control modeling of Manufacturing Operations data-driven modeling Optimization Design of Dynamic Experiments Dynamic Response Surface Model

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Automated data-driven modeling of building energy systems via machine learning algorithms

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ENERGY AND BUILDINGS 2019年 202卷 109384-000页

作者： Raetz, Martin Javadi, Amir Pasha Baranski, Marc Finkbeiner, Konstantin Mueller, Dirk Rhein Westfal TH Aachen EON Energy Res Ctr Inst Energy Efficient Bldg & Indoor Climate Aachen Germany

System modeling is a vital part of building energy optimization and control. Grey and white box modeling requires knowledge about the system and a lot of human assistance, which results in costs. In the common case, that information about the system is lacking, the feasibility of grey and white box models decreases further. The installation of sensors and the availability of monitoring data is growing rapidly within building energy systems. This enables the exploitation of statistical modeling, which is already well established in other sectors like computer science and finance. Thus, the present work investigates data-driven machine learning models to explore their potential for modeling building energy systems. The focus is to develop an efficient methodology for data-driven modeling. For this purpose, a comprehensive literature review for detecting optimization methods is conducted. Furthermore, the methodology is implemented in Python and an automated modeling tool is designed. It is used to model various energy systems based on monitoring data;seven use cases on three different systems reveal good results. The models can be used for forecasting, potential analysis, the implementation of various control strategies or as a replacement for missing information within the field of grey box modeling. (c) 2019 Elsevier B.V. All rights reserved.

关键词： Building energy modeling data-driven modeling Automated machine learning Building automation and control Regression Time-series forecasting

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Enhanced uniform data sampling for constrained data-driven modeling of antenna input characteristics

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2019年第5期32卷 e2584-e2584页

作者： Koziel, Slawomir Sigurdsson, Ari T. Pietrenko-Dabrowsk, Anna Szczepanski, Stanislaw Reykjav Univ Engn Optimizat & Modeling Ctr IS-103 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

data-driven surrogates are the most popular replacement models utilized in many fields of engineering and science, including design of microwave and antenna structures. The primary practical issue is a curse of dimensionality, which limits the number of independent parameters that can be accounted for in the modeling process. Recently, a performance-driven modeling technique has been proposed where the constrained domain of the model is spanned by a set of reference designs optimized with respect to selected figures of interest. This approach allows for significant improvement of prediction power of the surrogates without the necessity of reducing the parameter ranges. Yet uniform allocation of the training data samples in the constrained domain remains a problem. Here, a novel design of experiments technique ensuring better sample uniformity is proposed. Our approach involves uniform sampling on the domain-spanning manifold and linear transformation of the remaining sample vector components onto orthogonal directions with respect to the manifold. Two antenna examples are provided to demonstrate the advantages of the technique, including application case studies (antenna optimization).

关键词： antenna design constrained modeling data-driven modeling design of experiments simulation-based design uniform sampling

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data-driven modeling and real-time distributed control for energy efficient manufacturing systems

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ENERGY 2017年 127卷 247-257页

作者： Zou, Jing Chang, Qing Arinez, Jorge Xiao, Guoxian SUNY Stony Brook Dept Mech Engn Stony Brook NY 11794 USA Gen Motors Corp Gen Motors R&D Warren MI 48090 USA

As manufacturers face the challenges of increasing global competition and energy saving requirements, it is imperative to seek out opportunities to reduce energy waste and overall cost. In this paper, a novel data-driven stochastic manufacturing system modeling method is proposed to identify and predict energy saving opportunities and their impact on production. A real-time distributed feedback production control policy, which integrates the current and predicted system performance, is established to improve the overall profit and energy efficiency. A case study is presented to demonstrate the effectiveness of the proposed control policy. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Energy control methodology data-driven modeling Manufacturing system diagnosis and prognosis Feedback control Distributed control Energy profit optimization

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data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt

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AIN SHAMS ENGINEERING JOURNAL 2017年第4期8卷 549-557页

作者： Khadr, Mosaad Elshemy, Mohamed Tanta Univ Irrigat & Hydraul Engn Dept Fac Engn Tanta 31734 Egypt

Manzala Lake, the largest of the Egyptian lakes, is affected qualitatively and quantitatively by drainage water that flows into the lake. This study investigated the capabilities of adaptive neuro-fuzzy inference system (ANFIS) to predict water quality parameters of drains associated with Manzala Lake, with emphasis on total phosphorus and total nitrogen. A combination of data sets was considered as input data for ANFIS models, including discharge, pH, total suspended solids, electrical conductivity, total dissolved solids, water temperature, dissolved oxygen and turbidity. The models were calibrated and validated against the measured data for the period from year 2001 to 2010. The performance of the models was measured using various prediction skill criteria. Results show that ANFIS models are capable of simulating the water quality parameters and provided reliable prediction of total phosphorus and total nitrogen, thus suggesting the suitability of the proposed model as a tool for onsite water quality evaluation. (C) 2016 Ain Shams University. Production and hosting by Elsevier B.V.

关键词： data-driven modeling Water quality parameters Manzala Lake Egypt

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data-driven identification and modeling of earthquake-excited building structures regarding soil-structure interaction

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2020年第18期29卷 e1808-e1808页

作者： Gong, Nan Li, Peizhen Shan, Jiazeng Ouyang, Yuting Tongji Univ Dept Disaster Mitigat Struct Shanghai 200092 Peoples R China

data-driven identification and modeling of the integrated soil-structure interaction (SSI) structure system are significantly important for real-world structures subjected to earthquakes in various engineering disciplines. The representative mechanical modeling for the overall system with SSI effect is firstly presented, and the differential evolution algorithm is introduced for model-based iterative identification on physical parameters including stiffness and damping coefficients. The performance and reliability of the proposed methodology are systematically investigated through a numerical model-based parametric study. Incorporation of the model selection, the incomplete measurement and noise effect on the proposed method is revealed and discussed subsequently. Then, the present method is experimentally investigated and validated using two illustrative examples, corresponding to a 12-story large-scale structural model and a nine-story real-world building structure. The performance in terms of feasible estimation and reliable prediction by using the optimally identified physical model is demonstrated through the time history comparison of measurable acceleration responses in different seismic events.

关键词： data-driven modeling decentralized substructure differential evolution earthquake soil-structure interaction system identification

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Evolving General Regression Neural Networks using Limited Incremental Evolution for data-driven modeling of Non-linear Dynamic Systems 8

Evolving General Regression Neural Networks using Limited In...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Al-Mahasneh, Ahmad Jobran Anavatti, Sreenatha G. Garratt, Matthew A. Pratama, Mahardhika Univ New South Wales Canberra Sch Engn & Informat Technol Canberra ACT 2612 Australia

ISBN: (纸本)9781538692769

In this paper, an evolutionary general regression neural network is developed based on limited incremental evolution and distance-based pruning to online model dynamic systems. Also, a variance-based method is suggested to adapt the smoothing parameter in GRNN for online applications. The proposed model is compared with different types of dynamic neural networks. A nonlinear benchmarking dynamic discrete system with white Gaussian noise is used in the comparison. The results are compared in terms of the prediction error and the time required for adaption and the comparison remits show that the proposed model is more accurate and quicker than any another counterpart.

关键词： General Regression Neural Network online modeling data-driven modeling evolutionary neural networks

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data-driven modeling and Rendering of Force Responses from Elastic Tool Deformation

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SENSORS 2018年第1期18卷 237页

作者： Abdulali, Arsen Rakhmatov, Ruslan Ogay, Tatyana Jeon, Seokhee Kyung Hee Univ Dept Comp Engn Yongin 446701 South Korea

This article presents a new data-driven model design for rendering force responses from elastic tool deformation. The new design incorporates a six-dimensional input describing the initial position of the contact, as well as the state of the tool deformation. The input-output relationship of the model was represented by a radial basis functions network, which was optimized based on training data collected from real tool-surface contact. Since the input space of the model is represented in the local coordinate system of a tool, the model is independent of recording and rendering devices and can be easily deployed to an existing simulator. The model also supports complex interactions, such as self and multi-contact collisions. In order to assess the proposed data-driven model, we built a custom data acquisition setup and developed a proof-of-concept rendering simulator. The simulator was evaluated through numerical and psychophysical experiments with four different real tools. The numerical evaluation demonstrated the perceptual soundness of the proposed model, meanwhile the user study revealed the force feedback of the proposed simulator to be realistic.

关键词： haptic rendering tool deformation data-driven modeling force feedback

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