610 research outputs found
Dynamic modeling and optimization of cyanobacterial C-phycocyanin production process by artificial neural network
This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.algal.2015.11.004Artificial neural networks have been widely applied in bioprocess simulation and control due to their advantageous properties. However, their feasibility in long-term photo-fermentation process modelling and prediction as well as their efficiency on process optimisation have not been well studied so far. In the current study, an artificial neural network was constructed to simulate a 15-day fed-batch process for cyanobacterial C-phycocyanin production, which to the best of our knowledge has never been conducted. To guarantee the accuracy of artificial neural network, two strategies were implemented. The first strategy is to generate artificial data sets by adding random noise to the original data set, and the second is to choose the change of state variables as training data output. In addition, the first strategy showed the distinctive advantage of reducing the experimental effort in generating training data. By comparing with current experimental results, it is concluded that both strategies give the network great modelling and predictive power to estimate the entire fed-batch process performance, even when few original experimental data are supplied. Furthermore, by optimising the operating conditions of a 12-day fed-batch process, a significant increase of 85.6% on C-phycocyanin production was achieved compared to previous work, which suggests the high efficiency of artificial neural network on process optimisation.Author E. A. del Rio-Chanona is funded by CONACyT scholarship No. 522530 from the Secretariat of Public Education and the Mexican government. Author D. Zhang gratefully acknowledges the support from his family. This work was also supported by the National High Technology Research and Development Program 863, China (No. 2014AA021701) and the National Marine Commonwealth Research Program, China (No. 201205020-2)
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νμλ€.1. Introduction 26
1.1 Background and motivation 26
1.2 Literature review 28
1.2.1 Real time optimization 28
1.2.2 Optimality loss by model-plant mismatch 32
1.2.3 Methods to overcome the model-plant mismatch 33
1.3 Major contributions of this thesis 42
1.4 Outline of this thesis 44
2. Data-driven optimization via modifier adaptation 45
2.1 Introduction 45
2.2 Satisfaction of necessary conditions of optimality 47
3. Three issues of modifier adaptation 50
3.1 Issue 1: Frequent and large disturbance 50
3.1.1 Design of feedforward decision maker using machine learning and historical disturbance data 50
3.1.2 Illustrative example 70
3.1.3 Run-to-run optimization of bioprocess 82
3.1.4 Concluding remarks 88
3.2 Issue 2: Experimental gradient estimation under noisy and multivariate condition 89
3.2.1 Importance of gradient estimation for the modifier adaptation 89
3.2.2 Motivational example: Run-to-run optimization of bioreactor 91
3.2.3 Conventional experimental gradient estimation 96
3.2.4 Regression based gradient estimation and its application to modifier adaptation 99
3.2.5 Concluding remarks 129
3.3 Issue 3: A novel structure of modifier adaptation for robustness 130
3.3.1 Feasibility and structural robustness 130
3.3.2 Proposition of new structural modifier adaptation 135
3.3.3 Illustrative example 149
3.3.4 Concluding remarks 155
4. Conclusions and future works 156
4.1 Conclusions 156
4.2 Future works 157Docto
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Machine Learning Based Applications for Data Visualization, Modeling, Control, and Optimization for Chemical and Biological Systems
This dissertation report covers Yan Maβs Ph.D. research with applicational studies of machine learning in manufacturing and biological systems. The research work mainly focuses on reaction modeling, optimization, and control using a deep learning-based approaches, and the work mainly concentrates on deep reinforcement learning (DRL). Yan Maβs research also involves with data mining with bioinformatics. Large-scale data obtained in RNA-seq is analyzed using non-linear dimensionality reduction with Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), followed by clustering analysis using k-Means and Hierarchical Density-Based Spatial Clustering with Noise (HDBSCAN). This report focuses on 3 case studies with DRL optimization control including a polymerization reaction control with deep reinforcement learning, a bioreactor optimization, and a fed-batch reaction optimization from a reactor at Dow Inc.. In the first study, a data-driven controller based on DRL is developed for a fed-batch polymerization reaction with multiple continuous manipulative variables with continuous control. The second case study is the modeling and optimization of a bioreactor. In this study, a data-driven reaction model is developed using Artificial Neural Network (ANN) to simulate the growth curve and bio-product accumulation of cyanobacteria Plectonema. Then a DRL control agent that optimizes the daily nutrient input is applied to maximize the yield of valuable bio-product C-phycocyanin. C-phycocyanin yield is increased by 52.1% compared to a control group with the same total nutrient content in experimental validation. The third case study is employing the data-driven control scheme for optimization of a reactor from Dow Inc, where a DRL-based optimization framework is established for the optimization of the Multi-Input, Multi-Output (MIMO) reaction system with reaction surrogate modeling. Yan Maβs research overall shows promising directions for employing the emerging technologies of data-driven methods and deep learning in the field of manufacturing and biological systems. It is demonstrated that DRL is an efficient algorithm in the study of three different reaction systems with both stochastic and deterministic policies. Also, the use of data-driven models in reaction simulation also shows promising results with the non-linear nature and fast computational speed of the neural network models
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