Estimation of Chlamydomonas reinhardtii biomass concentration from chord length distribution data

Lopez-Exposito, Patricio; Suárez, Ángeles Blanco; Negro, Carlos

doi:10.1007/s10811-015-0749-4

Cited by 8 publications

(5 citation statements)

References 20 publications

(19 reference statements)

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“…In this context, ANNs have been treated as a powerful computational tool, since they can deal with nonlinear relationships, which are typical for most ecological relationships (Gevrey et al 2003;Gebler et al 2014). Artificial neural networks have been used in studies on organisms from freshwater ecosystems (e.g., Park et al 2003;Penczak et al 2012;Gebler et al 2014;Lopez-Exposito et al 2016); however, no further study has yet been conducted to confirm the performance of this approach in predicting the structure (richness and abundance) of lotic macroalgae communities, which are among the most important primary producers and are promising bioindicators of the trophic state in continental running water (Branco and Pereira 2002;Peres et al 2010;Cantonati et al 2012;Stancheva et al 2012). In this study, we showed that some architectures of multilayer ANNs were relatively efficient in predicting the species richness and abundance of macroalgae, even in high dynamic systems such as streams.…”

Section: Discussionmentioning

confidence: 99%

Modeling the species richness and abundance of lotic macroalgae based on habitat characteristics by artificial neural networks: a potentially useful tool for stream biomonitoring programs

et al. 2017

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One of the major challenges in stream ecology is the development of computational models that can predict aspects of the community structure of organisms from these ecosystems when they are subject to natural or artificial environmental fluctuations. To contribute towards this aim, we conducted a study whose main goal was to evaluate the efficiency and accuracy of different architectures of multilayer artificial neural networks (ANNs) in predicting the species richness and abundance of macroalgae based on environmental variables of tropical streams. We used data from 82 streams located in southern Brazil, where species richness, macroalgal abundance, and environmental parameters were measured. A set of 20 environmental parameters measured directly in the stream was used as explanatory variables. The performance of the ANN architectures was assessed using two different pieces of software (random combinatorial and exhaustive) and the coefficient of determination (R 2) and mean-squared error (MSE). For both species richness and macroalgal abundance, the best ANN architectures were obtained using random combination software and the performance parameters showed a combination of high R 2 and very low MSE. Our results suggest that computational models that are constructed based on ANN frameworks can be efficient and accurate in predicting the species richness and abundance of stream macroalgae from environmental data. Therefore, considering that models based on linear relationships have often failed, we recommend the application of ANNs as a tool to estimate species richness and abundance of lotic macroalgae from environmental data, in the management, conservation, and biomonitoring programs of tropical stream ecosystems.

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Section: Discussionmentioning

confidence: 99%

Modeling the species richness and abundance of lotic macroalgae based on habitat characteristics by artificial neural networks: a potentially useful tool for stream biomonitoring programs

et al. 2017

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“…Other estimated variables are the pigment and lipid content, and for their estimation, physical measurements of cell count, nitrate and glucose concentration are employed, complemented by process signals obtained by various methods as measurement of turbidity, IR spectrum and fluorescence, RGB imaging and transmission spectra, NMR spectroscopy, fluorescence, hyperspectral or RGB imaging, and dielectrophoresis. [22] (2) (a) [23,24] (2) (b) [25] Color analysis (RGB) Biomass On Off…”

Section: Measurement Methods Used For On-line Monitoring Of Biological Variablesmentioning

confidence: 99%

“…Lopez-Exposito et al [23] used a focused beam reflectance probe (FBRM) [64] which measures the chord length distribution (CLD), to represent the particle length distribution and to estimate the particle size. Data were acquired in samples drawn from a PBR and were processed by a perceptron (ANN) that correlated the measured CLD with the BC obtained by gravimetry, attaining a correlation coefficient of R 2 = 0.92.…”

Section: Methods Based On Reflectance Measurementmentioning

confidence: 99%

“…In a further development of this method [24], reflectance data obtained by the same rotating laser probe and BC data of C. sorokiniana in samples drawn from a PBR were processed with a support vector regression and a random forest regression model, both methods of machine learning, attaining the respective accuracy of R 2 = 0.87 and 0.81 in the biomass dry weight estimation. As in [23], flocculant was used in parallel to check the influence of the aggregation state and turbidity on the biomass estimation result.…”

Section: Methods Based On Reflectance Measurementmentioning

confidence: 99%

“…Lopez-Exposito et al [23] measured reflectance and processed data by a perceptron (ANN) estimating the biomass concentration with a correlation coefficient of R 2 = 0.92. In [24], reflectance data processed by two chemometric models estimated the biomass concentration with accuracy of R 2 = 0.81-0.87 (for more detail, see Section 2.3.4).…”

Section: Machine Learning Artificial Neural Networkmentioning

confidence: 99%

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On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

et al. 2022

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Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility.

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Software sensors in the monitoring of microalgae cultivations

Porras Reyes,

Havlik,

Beutel

2024

Rev Environ Sci Biotechnol

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Microalgae are well-known photosynthetic microorganisms used as cell factories for the production of relevant biotechnological compounds. Despite the outstanding characteristics attributed to microalgae, their industrial-scale production still struggles with scale-up problems and economic feasibility. One important bottleneck is the lack of suitable online sensors for the reliable monitoring of biological parameters, mostly concentrations of intracellular components, in microalgae bioprocesses. Software sensors provide an approach to improving the monitoring of those process parameters that are difficult to quantify directly and are therefore only indirectly accessible. Their use aims to improve the productivity of microalgal bioprocesses through better monitoring, control and automation, according to the current demands of Industry 4.0. In this review, a description of the microalgae components of interest as candidates for monitoring in a cultivation, an overview of software sensors, some of the available approaches and tools, and the current state-of-the-art of the design and use of software sensors in microalgae cultivation are presented. The latter is grouped on the basis of measurement methods used as software sensor inputs, employing either optical or non-optical techniques, or a combination of both. Some examples of software sensor design using simulated process data are also given, grouped according to their design, either as model-driven or data-driven estimators.

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Estimation of Chlamydomonas reinhardtii biomass concentration from chord length distribution data

Cited by 8 publications

References 20 publications

Modeling the species richness and abundance of lotic macroalgae based on habitat characteristics by artificial neural networks: a potentially useful tool for stream biomonitoring programs

Modeling the species richness and abundance of lotic macroalgae based on habitat characteristics by artificial neural networks: a potentially useful tool for stream biomonitoring programs

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

Software sensors in the monitoring of microalgae cultivations

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