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You searched for: Journal Industrial & engineering chemistry process design and development Remove constraint Journal: Industrial & engineering chemistry process design and development Subject case studies Remove constraint Subject: case studies Subject process design Remove constraint Subject: process design Subject algorithms Remove constraint Subject: algorithms Subject statistical analysis Remove constraint Subject: statistical analysis
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1. Expectation Maximization Approach for Simultaneous Gross Error Detection and Data Reconciliation Using Gaussian Mixture Distribution

Author:
Hashem Alighardashi; Nabil Magbool Jan; Biao Huang
Source:
Industrial & engineering chemistry process design and development 2017 v.56 no.49 pp. 14530-14544
ISSN:
1520-5045
Subject:
algorithms; case studies; models; process design; statistical analysis
Abstract:
... Process measurements play a significant role in process identification, control, and optimization. However, they are often corrupted by two types of errors, random and gross errors. The presence of gross errors in the measurements affects the reliability of optimization and control solutions. Therefore, in this work, we characterize the measurement noise model using a Gaussian mixture distribution ...
DOI:
10.1021/acs.iecr.7b02930

2. Process Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation

Author:
Jingxin Zhang; Maoyin Chen; Hao Chen; Xia Hong; Donghua Zhou
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.14 pp. 5579-5587
ISSN:
1520-5045
Subject:
algorithms; case studies; process design; process monitoring; statistical analysis
Abstract:
... By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP–MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, t ...
DOI:
10.1021/acs.iecr.8b05875

3. Data-Driven Model Based Computation and Analysis of Operability Sets Using High-Dimensional Continuation: A Plant-Wide Case Study

Author:
Utkarsh Konge; Abhishek Baikadi; Jayanth Mondi; Sivakumar Subramanian
Source:
Industrial & engineering chemistry process design and development 2020 v.59 no.21 pp. 10043-10060
ISSN:
1520-5045
Subject:
algorithms; case studies; chemistry; models; process design; statistical analysis
Abstract:
... A framework with a data-driven model based approach to compute and analyze the operability sets is presented. The computationally expensive steady-state simulations are replaced by machine learning models developed through statistical analysis of selected rigorous simulations. The entire solution manifold is computed using a high-dimensional continuation algorithm. The framework is applied to a pl ...
DOI:
10.1021/acs.iecr.9b07087