610 research outputs found

    Dynamic modeling and optimization of cyanobacterial C-phycocyanin production process by artificial neural network

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    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)

    λͺ¨λΈ 곡정 뢈일치 μƒν™©μ—μ„œ ν™”ν•™ 생물 κ³΅μ •μ˜ 데이터 기반 μ΅œμ ν™”λ₯Ό μœ„ν•œ κ°œμ„ ν•­ 적응법

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 화학생물곡학뢀, 2018. 2. 이쒅민.κ°œμ„ ν•­ 적응법은 데이터 기반 μ΅œμ ν™” κΈ°λ²•μ˜ μΌμ’…μœΌλ‘œ λͺ¨λΈ 곡정 뢈일치 μ‘°κ±΄μ—μ„œλ„ μˆ˜λ ΄ν•˜λŠ” 값이 κ³΅μ •μ˜ 졜적 ν•„μš” 쑰건을 λ§Œμ‘±ν•œλ‹€λŠ” νŠΉμ§•μ΄ μžˆλ‹€. 이 ν•™μœ„ 논문은 κ°œμ„ ν•­ μ μ‘λ²•μ˜ ν™”ν•™ 및 생물 곡정에 λŒ€ν•œ 적용 κ³Όμ •μ—μ„œ λ°œμƒν•˜λŠ” 3 가지 λ¬Έμ œμ μ— λŒ€ν•œ 해결책을 μ œμ‹œν•œλ‹€. 첫 번째, 반볡적으둜 λ°œμƒν•˜λŠ” 큰 μ™Έλž€μ— μ˜ν•œ μ΅œμ μ„± μƒμ‹€μ˜ λ¬Έμ œλŠ” κ³Όκ±° μ™Έλž€ 정보λ₯Ό μ΄μš©ν•˜μ—¬ μ•ž λ¨Ήμž„ κ²°μ •κΈ°λ₯Ό λ””μžμΈ ν•¨μœΌλ‘œμ¨ λΉ λ₯΄κ²Œ μ™Έλž€μ— λŒ€μ²˜ν•  수 μžˆλ‹€. μ΄λŸ¬ν•œ μ•ž λ¨Ήμž„ κ²°μ •κΈ°λŠ” μ΅œμ‹  기법인 심측 신경망 기법을 μ‚¬μš©ν•˜μ—¬ κ΅¬μ„±ν•˜μ˜€λ‹€. 두 번째, fed-batch reactor κ³΅μ •μ˜ 동적 μ΅œμ ν™” λ¬Έμ œμ™€ 같이 μ‘°μž‘ λ³€μˆ˜μ˜ μˆ˜κ°€ λ§Žμ€ μƒν™©μ—μ„œ λͺ©μ  ν•¨μˆ˜μ™€ μ œμ•½ 쑰건의 μ‹€ν—˜μ  ꡬ배λ₯Ό 효과적으둜 μΆ”μ •ν•˜κΈ° μœ„ν•˜μ—¬ νšŒκ·€ 뢄석 방법을 μ μš©ν•˜λŠ” 방법을 μ œμ•ˆν•˜μ˜€λ‹€. 이λ₯Ό μœ„ν•˜μ—¬ multiple linear regression (MLR), principle component analysis (PCA), partial least squares (PLS)와 같은 λ‹€μ–‘ν•œ νšŒκ·€ 뢄석 방법이 μ μš©λ˜μ—ˆκ³ , 보수적인 좔정을 μœ„ν•œ moving average μ—…λ°μ΄νŠΈ 방법도 μ œμ•ˆλ˜μ–΄ μˆ˜λ ΄ν–ˆμ„ λ•Œμ˜ κ³΅μ •μ˜ 졜적 ν•„μš” 쑰건 λ§Œμ‘±μ΄λΌλŠ” νŠΉμ„±μ„ μœ μ§€ν•¨μ„ 증λͺ…ν•˜μ˜€λ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ, μ—…λ°μ΄νŠΈμ—μ„œ λ°œμƒν•  수 μžˆλŠ” infeasible solutionκ³Ό 곡정 λ…Έμ΄μ¦ˆλ₯Ό 효과적으둜 μ²˜λ¦¬ν•  수 μžˆλŠ” μƒˆλ‘œμš΄ ν˜•νƒœμ˜ κ°œμ„ ν•­ 적응법을 μ œμ•ˆν•˜μ˜€λ‹€. λ˜ν•œ μ œμ•ˆλœ μƒˆλ‘œμš΄ ꡬ쑰의 κ°œμ„ ν•­ 적응법이 κ°–λŠ” λ…Έμ΄μ¦ˆμ— λŒ€ν•œ 강건성과 μˆ˜λ ΄μ„±, 그리고 μˆ˜λ ΄ν–ˆμ„ λ•Œμ˜ 졜적 ν•„μš”μ‘°κ±΄μ΄ λ§Œμ‘±ν•¨μ„ 증λͺ…ν•˜μ˜€λ‹€.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

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    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

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    This dissertation report covers Yan Ma’s 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 Ma’s 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 Ma’s 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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