Optimal Operation Strategy for Biohydrogen Production

Self Cite

Lutein is a high-value bioproduct synthesized by microalga Desmodesmus sp. It has great potential for the food, cosmetics, and pharmaceutical industries. However, in order to enhance its productivity and to fulfil its ever-increasing global market demand, it is vital to construct accurate models capable of simulating the entire behavior of the complicated dynamics of the underlying biosystem. To this aim, in this study two highly robust artificial neural networks (ANNs) are designed for the first time. Contrary to conventional ANNs, these networks model the rate of change of the dynamic system, which makes them highly relevant in practice. Different strategies are incorporated into the current research to guarantee the accuracy of the constructed models, which include determining the optimal network structure through a hyper-parameter selection framework, generating significant amounts of artificial data sets by embedding random noise of appropriate size, and rescaling model inputs through standardization. Based on experimental verification, the high accuracy and great predictive power of the current models for long-term dynamic bioprocess simulation in both real-time and offline frameworks are thoroughly demonstrated. This research, therefore, paves the way to significantly facilitate the future investigation of lutein bioproduction process control and optimization. In addition, the model construction strategy developed in this research has great potential to be directly applied to other bioprocesses. Biotechnol. Bioeng. 2017;114: 2518-2527. © 2017 Wiley Periodicals, Inc.

Section: Resultsmentioning

confidence: 99%

An efficient model construction strategy to simulate microalgal lutein photo‐production dynamic process

Rio‐Chanona

Fiorelli

Zhang

et al. 2017

Self Cite

“…The covariance matrix for the estimated parameters is approximated by the inverse of the reduced Hessian at the optimal solution. Confidence intervals are then obtained from the trace of this approximated covariance matrix following standard procedures (del Rio‐Chanona et al, ). However, as a result of the high non‐linearity and complexity of modeling metabolic kinetics, the assumption of computing the confidence intervals from the above framework may not hold.…”

Section: Resultsmentioning

confidence: 99%

Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production

Rio‐Chanona

Liu

Wagner

et al. 2017

Self Cite

Biodiesel produced from microalgae has been extensively studied due to its potentially outstanding advantages over traditional transportation fuels. In order to facilitate its industrialization and improve the process profitability, it is vital to construct highly accurate models capable of predicting the complex behavior of the investigated biosystem for process optimization and control, which forms the current research goal. Three original contributions are described in this paper. Firstly, a dynamic model is constructed to simulate the complicated effect of light intensity, nutrient supply and light attenuation on both biomass growth and biolipid production. Secondly, chlorophyll fluorescence, an instantly measurable variable and indicator of photosynthetic activity, is embedded into the model to monitor and update model accuracy especially for the purpose of future process optimal control, and its correlation between intracellular nitrogen content is quantified, which to the best of our knowledge has never been addressed so far. Thirdly, a thorough experimental verification is conducted under different scenarios including both continuous illumination and light/dark cycle conditions to testify the model predictive capability particularly for long-term operation, and it is concluded that the current model is characterized by a high level of predictive capability. Based on the model, the optimal light intensity for algal biomass growth and lipid synthesis is estimated. This work, therefore, paves the way to forward future process design and real-time optimization.

“…Thus, the objective function for parameter estimation, Equation (5), is designed such that in addition to minimising residues between model prediction and experimental data, it also penalises the total number of active binary variables in the polynomial terms to avoid overfitting. In this way, the optimal hybrid model structure alongside its parameter values can be simultaneously identified via the established dynamic parameter estimation algorithm (del Rio‐Chanona et al, 2015):

\min F = \sum_{k = 1}^{n} \sum_{p = 1}^{m} \sum_{i = 1}^{3} {false(c_{i, p, k, E} - c_{i, p, k, M} false)}^{2} \cdot w_{i, j, k} + w_{b} \cdot \sum_{j = 0}^{4} \sum_{i = 1}^{3} b_{ij},

where

c_{i, p, k, E}

and

c_{i, p, k, M}

are experimental and model‐simulated value for concentration of state

S_{i}

at time step

p \in m

in the k th data set (total number of data sets is

n

), respectively,

w_{i, j, k}

and

w_{b}

are the weight for each data point and the sum of binary variables, respectively.…”

Section: Methodsmentioning

confidence: 99%

Combining model structure identification and hybrid modelling for photo‐production process predictive simulation and optimisation

Zhang

Savage

Cho

2020

Self Cite

Integrating physical knowledge and machine learning is a critical aspect of developing industrially focused digital twins for monitoring, optimisation, and design of microalgal and cyanobacterial photo-production processes. However, identifying the correct model structure to quantify the complex biological mechanism poses a severe challenge for the construction of kinetic models, while the lack of data due to the time-consuming experiments greatly impedes applications of most data-driven models. This study proposes the use of an innovative hybrid modelling approach that consists of a simple kinetic model to govern the overall process dynamic trajectory and a data-driven model to estimate mismatch between the kinetic equations and the real process. An advanced automatic model structure identification strategy is adopted to simultaneously identify the most physically probable kinetic model structure and minimum number of data-driven model parameters that can accurately represent multiple data sets over a broad spectrum of process operating conditions. Through this hybrid modelling and automatic structure identification framework, a highly accurate mathematical model was constructed to simulate and optimise an algal lutein production process. Performance of this hybrid model for long-term predictive modelling, optimisation, and online self-calibration is demonstrated and thoroughly discussed, indicating its significant potential for future industrial application.