Ouyang Wu

Ouyang Wu
Hochschule für Angewandte Wissenschaften Hamburg | HAW · Department of Mechanical Engineering and Production

Doctor of Philosophy

About

19
Publications
1,159
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88
Citations

Publications

Publications (19)
Article
In the era, that data collection is not as challenging as before, data-driven process modeling for prediction of unmeasurable or expensive-to-measure variables is gaining popularity. Probabilistic principal component analysis has powerful features for modeling such as considering uncertainty and dealing with high-dimensional process data. Although...
Article
Full-text available
Energy system optimization models are typically large models which combine sub-models which range from linear to very nonlinear. Column generation (CG) is a classical tool to generate feasible solutions of sub-models, defining columns of global master problems, which are used to steer the search for a global solution. In this paper, we present a n...
Presentation
Full-text available
In this talk we present Decogo, a generic software framework for solving sparse nonconvex MINLPs, based on decomposition based successive approximation. Similar as Column Generation (CG) algorithms for solving huge crew scheduling problems, Decogo computes a solution candidate of a MINLP by first computing a solution of a convex hull relaxation (CR...
Article
This paper considers joint production and maintenance scheduling of a multiproduct batch chemical manufacturing plant. A Generalized Disjunctive Programming-based formulation is proposed for the scheduling problem, integrating additional features inspired by an industrial case study, namely sequence-dependent degradation and limited final product s...
Article
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The analysis of equipment degradation has traditionally developed in two main directions. One approach, of great interest for control system design, has been to consider that degradation causes fundamental changes to the behaviour of a system. Another approach, used in optimal maintenance planning and production scheduling, considers degradation as...
Article
Full-text available
Performance decay due to asset degradation is an important constraint in industrial production and therefore needs to be actively considered. This paper focuses on short-term scheduling for multiproduct batch processes with sequence-dependent degradation and is motivated by a case study in which the sequence of multiple-grade batch runs impacts evo...
Article
In this paper, modeling of storage constraints and material transfer are considered for short-term batch scheduling. The key features of storage and quality checks are inspired from a case study of a multiproduct batch plant. The case study presents two strategies for assigning batch orders to each individual storage tank during batch production, w...
Article
Full-text available
In the process industry, various types of degradation occur in processing plants, resulting in significant economic losses. Modeling of degradation is important because it provides quantitative insights for consideration of degradation impacts in the operations of process manufacturing. This paper studies batch processes that show a periodic patter...
Article
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Degradation such as fouling has significant negative impact on the efficiency of process manufacturing. Creating transparency about degradation is essential in the operations of the plants. This paper focuses on the state estimation of fouling in a multipurpose batch process, and the problem is formulated using an industrial case study. Due to the...
Chapter
In this paper, we present an industrial case study of a batch process; the degradation effect is fouling in heat exchangers which impedes heat transfer and fluid flow during batch production. The degradation increases from batch to batch until the batch reactor is shut down for a scheduled maintenance. That is, there is a periodic pattern in the ba...
Article
The parameter estimation for a class of single-input single-output (SISO) Hammerstein state space systems is considered in this paper. The nonlinear block in the discussed system is represented by a polynomial in the input signal with unknown coefficients. By applying the over-parameterization method, the SISO Hammerstein state space model is trans...
Conference Paper
Two different types of measurements are often available for the key quality variables in process industries - (a) an accurate “slow-rate” laboratory measurements, and (b) a less accurate “fast-rate” online analyser measurements. Also, the analyser measurements are prone to fail due to hardware issues. Therefore, the main objective of this work is t...
Article
With a large amount of industrial data available, it is of considerable interest to develop data-based models. The challenge lies in the significant noises that appear in all data collected from industry. The errors-in-variables (EIV) model is a model that accounts for measurement noises in all observations (both input and output). In most of the t...
Article
This paper proposes an augmented model approach for identification of nonlinear errors-in-variables (EIVs) systems. An EIV model accounts for uncertainties in the observations of both inputs and outputs. As the direct identification of nonlinear functions is difficult, we propose to approximate the nonlinear EIV model using multiple ARX models. To...
Article
In this work, one of the common issues, the robustness of the soft sensors, in development of such predictive models is discussed and the solution is provided. Large random errors, also known as outliers are one inseparable characteristic of data sets which can be caused by various reasons. Robust probabilistic predictive models overcome this probl...
Article
In this paper, Alberta electricity spot market or Power Pool pricing is studied and the pool price is modeled through a hidden Markov model and multiple local ARX models. By selecting and preprocessing the exogenous factors (e.g. the price forecast from Alberta Electric System Operator (AESO), demand forecast and so forth), a one-hour ahead predict...

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Projects

Projects (2)
Project
http://www.h2020pronto.eu/ The research topics proposed by the consortium partners are: - data analytics to extract information from large, heterogeneous assemblies of data to determine machinery condition and process performance; - optimization of operations, materials, energy and wastes taking machinery condition and process performance into account.
Project
The goals of this project are (i) development of new parallel decomposition methods for deterministic global optimization (ii) development of the new open-source MINLP-solver Decogo and (iii) solving difficult industrial optimization models using the new methods.