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Rendall R, Chiang LH, Reis MS. Data-driven methods for batch data analysis – A critical overview and mapping on the complexity scale. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.01.014] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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52
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Prabhu GRD, Witek HA, Urban PL. Telechemistry: monitoring chemical reactionsviathe cloud using the Particle Photon Wi-Fi module. REACT CHEM ENG 2019. [DOI: 10.1039/c9re00043g] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
A popular electronic module and the associated Internet-of-Things tools provide chemists with more control over long-term experimental procedures and enhance lab work safety.
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Affiliation(s)
- Gurpur Rakesh D. Prabhu
- Department of Applied Chemistry
- National Chiao Tung University
- Hsinchu
- Taiwan
- Department of Chemistry
| | - Henryk A. Witek
- Department of Applied Chemistry
- National Chiao Tung University
- Hsinchu
- Taiwan
- Center for Emergent Functional Matter Science
| | - Pawel L. Urban
- Department of Chemistry
- National Tsing Hua University
- Hsinchu
- Taiwan
- Frontier Research Center on Fundamental and Applied Sciences of Matters
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53
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Grajciar L, Heard CJ, Bondarenko AA, Polynski MV, Meeprasert J, Pidko EA, Nachtigall P. Towards operando computational modeling in heterogeneous catalysis. Chem Soc Rev 2018; 47:8307-8348. [PMID: 30204184 PMCID: PMC6240816 DOI: 10.1039/c8cs00398j] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Indexed: 12/19/2022]
Abstract
An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.
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Affiliation(s)
- Lukáš Grajciar
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
| | - Christopher J. Heard
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
| | - Anton A. Bondarenko
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
| | - Mikhail V. Polynski
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
| | - Jittima Meeprasert
- Inorganic Systems Engineering group
, Department of Chemical Engineering
, Faculty of Applied Sciences
, Delft University of Technology
,
Van der Maasweg 9
, 2629 HZ Delft
, The Netherlands
.
| | - Evgeny A. Pidko
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
- Inorganic Systems Engineering group
, Department of Chemical Engineering
, Faculty of Applied Sciences
, Delft University of Technology
,
Van der Maasweg 9
, 2629 HZ Delft
, The Netherlands
.
| | - Petr Nachtigall
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
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54
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Onel M, Kieslich CA, Guzman YA, Floudas CA, Pistikopoulos EN. Reprint of: Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.10.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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55
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Onel M, Kieslich CA, Guzman YA, Floudas CA, Pistikopoulos EN. Big Data Approach to Batch Process Monitoring: Simultaneous Fault Detection and Diagnosis Using Nonlinear Support Vector Machine-based Feature Selection. Comput Chem Eng 2018; 115:46-63. [PMID: 30386002 DOI: 10.1016/j.compchemeng.2018.03.025] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark dataset which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the prealigned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.
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Affiliation(s)
- Melis Onel
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Chris A Kieslich
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA.,Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Yannis A Guzman
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.,Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Christodoulos A Floudas
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Efstratios N Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
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56
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How to Generate Economic and Sustainability Reports from Big Data? Qualifications of Process Industry. Processes (Basel) 2017. [DOI: 10.3390/pr5040064] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Big Data may introduce new opportunities, and for this reason it has become a mantra among most industries. This paper focuses on examining how to develop cost and sustainable reporting by utilizing Big Data that covers economic values, production volumes, and emission information. We assume strongly that this use supports cleaner production, while at the same time offers more information for revenue and profitability development. We argue that Big Data brings company-wide business benefits if data queries and interfaces are built to be interactive, intuitive, and user-friendly. The amount of information related to operations, costs, emissions, and the supply chain would increase enormously if Big Data was used in various manufacturing industries. It is essential to expose the relevant correlations between different attributes and data fields. Proper algorithm design and programming are key to making the most of Big Data. This paper introduces ideas on how to refine raw data into valuable information, which can serve many types of end users, decision makers, and even external auditors. Concrete examples are given through an industrial paper mill case, which covers environmental aspects, cost-efficiency management, and process design.
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