scholarly journals Data-driven design of bi-selective OSDAs for intergrowth zeolites

2021 ◽  
Author(s):  
Daniel Schwalbe-Koda ◽  
Avelino Corma ◽  
Yuriy Román-Leshkov ◽  
Manuel Moliner ◽  
Rafael Gómez-Bombarelli
Keyword(s):  
Data Mining ◽  
Shape Selectivity ◽  
Industrial Applications ◽  
Inorganic Materials ◽  
Data Driven ◽  
Computational Simulations ◽  
Organic Structure ◽  
Phase Competition ◽  
Disordered Structure ◽  
End Members

Zeolites are inorganic materials with wide industrial applications due to their topological diversity. Tailoring confinement effects in zeolite pores, for instance by crystallizing intergrown frameworks, can improve their catalytic and transport properties, but controlling zeolite crystallization often relies on heuristics. In this work, we use computational simulations and data mining to design organic structure-directing agents (OSDAs) to favor the synthesis of intergrown zeolites. First, we propose design principles to identify OSDAs which are selective towards both end members of the disordered structure. Then, we mine a database of hundreds of thousands of zeolite-OSDA pairs and downselect OSDA candidates to synthesize known intergrowth zeolites such as CHA/AFX, MTT/TON, and BEC/ISV. The computationally designed OSDAs balance phase competition metrics and shape selectivity towards the frameworks, thus bypassing expensive dual-OSDA approaches typically used in the synthesis of intergrowths. Finally, we propose potential OSDAs to obtain hypothesized disordered frameworks such as AEI/SAV. This work may accelerate zeolite discovery through data-driven synthesis optimization and design.

scholarly journals Data-driven analysis of interactions between people with dementia and a tablet device

2017 ◽  
Vol 3 (2) ◽  
pp. 735-738
Author(s):  
Wolfgang Doneit ◽  
Jana Lohse ◽  
Kristina Glesing ◽  
Clarissa Simon ◽  
Monika Fischer ◽  
...  
Keyword(s):  
Data Mining ◽  
Facial Expressions ◽  
Music Videos ◽  
Data Driven ◽  
Tablet Devices ◽  
Log Files ◽  
People With Dementia ◽  
Analysis Strategy ◽  
Tablet Device ◽  
Data Mining Strategy

AbstractIn the project I-CARE a technical system for tablet devices is developed that captures the personal needs and skills of people with dementia. The system provides activation content such as music videos, biographical photographs and quizzes on various topics of interest to people with dementia, their families and professional caregivers. To adapt the system, the activation content is adjusted to the daily condition of individual users. For this purpose, emotions are automatically detected through facial expressions, motion, and voice. The daily interactions of the users with the tablet devices are documented in log files which can be merged into an event list. In this paper, we propose an advanced format for event lists and a data analysis strategy. A transformation scheme is developed in order to obtain datasets with features and time series for popular methods of data mining. The proposed methods are applied to analysing the interactions of people with dementia with the I-CARE tablet device. We show how the new format of event lists and the innovative transformation scheme can be used to compress the stored data, to identify groups of users, and to model changes of user behaviour. As the I-CARE user studies are still ongoing, simulated benchmark log files are applied to illustrate the data mining strategy. We discuss possible solutions to challenges that appear in the context of I-CARE and that are relevant to a broad range of applications.

Stamping Plant 4.0 – Basics for the Application of Data Mining Methods in Manufacturing Car Body Parts

2015 ◽  
Vol 639 ◽  
pp. 21-30 ◽  
Author(s):  
Stephan Purr ◽  
Josef Meinhardt ◽  
Arnulf Lipp ◽  
Axel Werner ◽  
Martin Ostermair ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Acquisition ◽  
Data Driven ◽  
Quality Analysis ◽  
Process Conditions ◽  
Data Sets ◽  
Body Parts ◽  
Car Body ◽  
Sample Data ◽  
Mining Methods

Data-driven quality evaluation in the stamping process of car body parts is quite promising because dependencies in the process have not yet been sufficiently researched. However, the application of data mining methods for the process in stamping plants would require a large number of sample data sets. Today, acquiring these data represents a major challenge, because the necessary data are inadequately measured, recorded or stored. Thus, the preconditions for the sample data acquisition must first be created before being able to investigate any correlations. In addition, the process conditions change over time due to wear mechanisms. Therefore, the results do not remain valid and a constant data acquisition is required. In this publication, the current situation in stamping plants regarding the process robustness will be first discussed and the need for data-driven methods will be shown. Subsequently, the state of technology regarding the possibility of collecting the sample data sets for quality analysis in producing car body parts will be researched. At the end of this work, an overview will be provided concerning how this data collection was implemented at BMW as well as what kind of potential can be expected.

scholarly journals Prospects of Biocatalyst Purification Enroute Fermentation Processes

2021 ◽  
Author(s):  
Michael Bamitale Osho ◽  
Sarafadeen Olateju Kareem
Keyword(s):  
Fermentation Process ◽  
Colloidal Particles ◽  
Methyl Cellulose ◽  
Industrial Applications ◽  
Maximum Activity ◽  
Inorganic Materials ◽  
Organic Substrates ◽  
Ammonium Sulphate Precipitation ◽  
Sodom Apple ◽  
Microbial Agents

Biotransformation of broth through fermentation process suffers a major setback when it comes to disintegration of organic substrates by microbial agents for industrial applications. These biocatalysts are in crude/dilute form hence needs to be purified to remove colloidal particles and enzymatic impurities thus enhancing maximum activity. Several contractual procedures of concentrating dilute enzymes and proteins had been reported. Such inorganic materials include ammonium sulphate precipitation; salting, synthetic polyacrylic acid; carboxy-methyl cellulose, tannic acid, edible gum and some organic solvents as precipitants etc. The emergence of organic absorbents such as sodom apple (Calostropis procera) extract, activated charcoal and imarsil had resulted in making significant impact in industrial circle. Various concentrations of these organic extracts have been used as purifying agents on different types of enzyme vis: lipase, amylase, protease, cellulase etc. Purification fold and stability of the enzyme crude form attained unprecedented results.

Domain Driven Data Mining

2008 ◽  
pp. 196-223 ◽  
Author(s):  
Longbing Cao ◽  
Chengqi Zhang
Keyword(s):  
Data Mining ◽  
Complex Systems ◽  
Real World ◽  
Domain Knowledge ◽  
Pattern Mining ◽  
Iterative Refinement ◽  
User Preference ◽  
Data Driven ◽  
Real World Data ◽  
Hidden Knowledge

Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual demonstration of specific algorithms cannot support business users to take actions to their advantage and needs. We think this is due to Quantitative Intelligence focused data-driven philosophy. It either views data mining as an autonomous data-driven, trial-and-error process, or only analyzes business issues in an isolated, case-by-case manner. Based on experience and lessons learnt from real-world data mining and complex systems, this article proposes a practical data mining methodology referred to as Domain-Driven Data Mining. On top of quantitative intelligence and hidden knowledge in data, domain-driven data mining aims to meta-synthesize quantitative intelligence and qualitative intelligence in mining complex applications in which human is in the loop. It targets actionable knowledge discovery in constrained environment for satisfying user preference. Domain-driven methodology consists of key components including understanding constrained environment, business-technical questionnaire, representing and involving domain knowledge, human-mining cooperation and interaction, constructing next-generation mining infrastructure, in-depth pattern mining and postprocessing, business interestingness and actionability enhancement, and loop-closed human-cooperated iterative refinement. Domain-driven data mining complements the data-driven methodology, the metasynthesis of qualitative intelligence and quantitative intelligence has potential to discover knowledge from complex systems, and enhance knowledge actionability for practical use by industry and business.

scholarly journals Data-Driven Modelling of Smart Building Ventilation Subsystem

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Grigore Stamatescu ◽  
Iulia Stamatescu ◽  
Nicoleta Arghira ◽  
Ioana Fagarasan
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Data Driven ◽  
Support Vector ◽  
Commercial Building ◽  
Monitoring And Control ◽  
Smart Building ◽  
Building Ventilation ◽  
Using Data ◽  
Rich Data

Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations, the application of conventional system identification or approximate grey-box models, partly theoretic and partly data driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data from over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

scholarly journals Data-Driven Approach for Incident Management in a Smart City

2020 ◽  
Vol 10 (22) ◽  
pp. 8281
Author(s):  
Luís B. Elvas ◽  
Carolina F. Marreiros ◽  
João M. Dinis ◽  
Maria C. Pereira ◽  
Ana L. Martins ◽  
...  
Keyword(s):  
Data Mining ◽  
Smart City ◽  
City Planning ◽  
Smart Cities ◽  
Data Driven ◽  
Incident Management ◽  
Cascading Effects ◽  
Data Mining Approach ◽  
Industry Standard ◽  
Data Driven Approach

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.

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