Neural network modeling for on-line estimation of nutrient dynamics in a sequentially-operated batch reactor.

In monitoring and controlling wastewater treatment processes, on-line information of nutrient dynamics is very important. However, these variables are determined with a significant time delay. Although the final effluent quality can be analyzed after this delay, it is often too late to make proper adjustments. In this paper, a neural network approach, a software sensor, was proposed to overcome this problem. Software sensor refers to a modeling approach inferring hard-to-measure process variables from other on-line measurable process variables. A bench-scale sequentially-operated batch reactor (SBR) used for advanced wastewater treatment (BOD plus nutrient removal) was employed to develop the neural network model. In order to improve the network performance, the structure of neural network was arranged in such a way of reflecting the change of operational conditions within a cycle. Real-time estimation of PO3-(4), NO-3, and NH+4 concentrations was successfully carried out with the on-line information of the SBR system only.     

A neural network estimator for total biomass of filamentous fungi growing on two dimensional solid substrate

A neural network dynamic model is proposed for the on-line estimation of total biomass during filamentous fungi cultures on two dimensional solid substrate. The neural network provides an accurate and robust estimation of biomass from macroscopic measurements of the colony radius evolution. Experiments were performed on Gibberella fujikuroi growing on Petri dishes under different conditions of temperature and water activity. © Rapid Science Ltd. 1998     

Estimation of fungal spore concentrations associated to meteorological variables

A 1-year time series of fungal spore concentrations has been used to calibrate an artificial neural network for the estimation of Alternaria and Pleospora concentrations associated to observed meteorological variables. Analysis of the results revealed that the daily average values of these meteorological variables are suitable to predict with high confidence the number of fungal spores that are actually observed. The calibrated neural network has also been used randomizing each single input parameter in order to evaluate which meteorological variable contributes more to the formation and the depletion of the selected fungal spores. Emphasis is given to the possibility of using the proposed model for operational activities, predicting the future spore concentrations on the basis of meteorological forecasts.     

An artificial neural network for biomass estimation from automatic pH control signal

This study developed an artificial neural network (ANN) to estimate the growth of microorganisms during a fermentation process. The ANN relies solely on the cumulative consumption of alkali and the buffer capacity, which were measured on-line from the on/off control signal and pH values through automatic pH control. The two input variables were monitored on-line from a series of different batch cultivations and used to train the ANN to estimate biomass. The ANN was refined by optimizing the network structure and by adopting various algorithms for its training. The software estimator successfully generated growth profiles that showed good agreement with the measured biomass of separate batch cultures carried out between at 25 and 35_C.     

Prediction of <Emphasis Type="Italic">Alternaria</Emphasis> and <Emphasis Type="Italic">Pleospora</Emphasis> concentrations from the meteorological forecast and artificial neural network in L’Aquila,Abruzzo (Central Italy)

In a previous work a 1-year time series of fungal spore concentrations was used to calibrate an artificial neural network for the estimation of Alternaria and Pleospora concentrations associated with observed meteorological variables in the atmosphere of L’Aquila, Italy. In this article the possibility to use the neural model calibrated with observed meteorological variables to predict the future fungal spore concentration from meteorological forecast is investigated. The results show that the proposed technique appears to be a suitable device to operationally predict the Alternaria and Pleospora concentrations a few days in advance. Emphasis is given to the actual use of these predictions for establishing a preventive strategy for allergy sufferers and for an appropriate use of fungicide treatments in agricultural activities, avoiding unsafe and useless pollution of the atmosphere, crops and fields.     

Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks

The information of nutrient dynamics is essential for the precise control of effluent quality discharged from biological wastewater treatment processes. However, these variables can usually be determined with a significant time delay. Although the final effluent quality can be analyzed after this delay, it is often too late to make proper adjustments. In this paper, a neural network approach, a software sensor, was proposed for the real-time estimation of nutrient concentrations and overcoming the problem of delayed measurements. In order to improve the neural network performance, a split network structure applied separately for anaerobic and aerobic conditions was employed with dynamic modeling methods such as auto-regressive with exogenous inputs. The proposed methodology was applied to a bench-scale sequencing batch reactor (SBR) for biological nutrient removal. The extrapolation problem of neural networks was possible to be partially overcome with the aid of multiway principal component analysis because of its ability of detecting of abnormal situations which could generate extrapolation. Real-time estimation of PO₄³⁻, NO₃⁻ and NH₄⁺ concentrations based on neural network was successfully carried out with the simple on-line information of the SBR system only.     

On-line state estimation of biomass based on acid production in Zymomonas mobilis cultures

The concentrations of biomass, substrate and product are very important state variables of almost every bioprocess and generally unable to be measured directly in?situ due to the lack of reliable sensors. In this paper, an adaptive observer of the biomass concentration is proposed for an anaerobic fermentation process where only the measurement of the acid product is available on-line. The observer was tested to be effective by several experiments under various operating conditions. In this experimental system, an auto-sampling device was connected between the bioreactor for the fermentation of Zymomonas mobilis and a HPLC so that the concentrations of glucose and ethanol could be directly measured through such implementation.     

On-line estimation of biological variables during pH controlled lactic acid fermentations

Indirect measurement of lactose, galactose, lactic acid, and biomass concentration from on-line sodium hydroxide weight measurements have been obtained for pure and mixed batch cultures of Streptococcus salivarius ssp. thermophilus 404 and Lactobacillus delbrueckii subsp. bulgaricus 398 conducted at controlled pH and temperature. Linear correlations were established between the equivalent sodium hydroxide concentration and the lactose (substrate), galactose and lactic acid (products) concentrations while nonlinear relationships were developed between biomass and lactic acid concentrations. These nonlinear relationships took into account the inhibitory effect of lactic acid on growth and acidification. The indirect measurements of biomass concentration were introduced into a nonlinear estimator of the state variables and of the specific growth and lactic acid production rates. Good agreement was found between estimated and measured biomass concentrations (error index ranging from 10.8% to 12.6%). The results showed the feasibility of on-line estimation of biomass concentration and of the specific kinetics from NaOH addition weight measurements and its applicability for monitoring lactic acid fermentations. Using off-line measurements of L(+) and D(-) lactic acid concentrations, the evolution of the concentration of each strain in mixed cultures was obtained from the relationships proposed for the mixed cultures. (c) 1994 John Wiley & Sons, Inc.     

Estimation of multiple biomass growth rates and biomass concentration in a class of bioprocesses

In this paper, an approach to the estimation of multiple biomass growth rates and biomass concentration is proposed for a class of aerobic bioprocesses characterized by on-line measurements of dissolved oxygen and carbon dioxide concentrations, as well as off-line measurements of biomass concentration. The approach is based on adaptive observer theory and includes two steps. In the first step, an adaptive estimator of two out of three biomass growth rates is designed. In the second step, the third biomass growth rate and the biomass concentration are estimated, using two different adaptive estimators. One of them is based on on-line measurements of dissolved oxygen concentration and off-line measurement of biomass concentrations, while the other needs only on-line measurements of the carbon dioxide concentration. Simulations demonstrated good performance of the proposed estimators under continuous and batch-fed conditions.     

Monitoring of fed-batch <Emphasis Type="Italic">E. coli</Emphasis> fermentations with software sensors

Accurate monitoring and control of industrial bioprocess requires the knowledge of a great number of variables, being some of them not measurable with standard devices. To overcome this difficulty, software sensors can be used for on-line estimation of those variables and, therefore, its development is of paramount importance. An Asymptotic Observer was used for monitoring Escherichia coli fed-batch fermentations. Its performance was evaluated using simulated and experimental data. The results obtained showed that the observer was able to predict the biomass concentration profiles showing, however, less satisfactory results regarding the estimation of glucose and acetate concentrations. In comparison with the results obtained with an Extended Kalman Observer, the performance of the Asymptotic Observer in the fermentation monitoring was slightly better.     

Neural networks for the optimization of crude oil blending

Crude oil blending is an important unit in petroleum refining industry. Many blend automation systems use real-time optimizer (RTO), which apply current process information to update the model and predict the optimal operating policy. The key unites of the conventional RTO are on-line analyzers. Sometimes oil fields cannot apply these analyzers. In this paper, we propose an off-line optimization technique to overcome the main drawback of RTO. We use the history data to approximate the output of the on-line analyzers, then the desired optimal inlet flow rates are calculated by the optimization technique. After this off-line optimization, the inlet flow rates are used for on-line control, for example PID control, which forces the flow rate to follow the desired inlet flow rates. Neural networks are applied to model the blending process from the history data. The new optimization is carried out via the neural model. The contributions of this paper are: (1) Stable learning for the discrete-time multilayer neural network is proposed. (2) Sensitivity analysis of the neural optimization is given. (3) Real data of a oil field is used to show effectiveness of the proposed method.     

Modeling <Emphasis Type="Italic">Euglena</Emphasis> sp. growth under different conditions using an artificial neural network

Microalgae are considered as the future source of biofuels because of their high biomass productivity and neutral lipid content as triacylglycerides (TAG). Microalgae have high photosynthetic efficiency and the possibility of being cultivated in different wastewaters. The isolation of potential microalgae followed by the optimization of cultivation conditions is prerequisite for successful cultivation and accumulation of high lipid content. In the present work, a three-layer artificial neural network (ANN) model is developed to predict the essential parameters (such as pH, temperature, light intensity, photoperiod, and medium composition) based on 156 sets of laboratory experiments for achieving maximum biomass from Euglena sp. The independent parameters (viz., temperature, light intensity, photoperiod and number of days at fixed pH, and media composition) were fed as input to the ANN, and biomass yield was investigated. The comparison of the simulated environmental conditions using the ANN model and experimental results are found to have an excellent correlation coefficient of about 0.97 for the model variables used in this study. The model results established that artificial neural network design may be judiciously employed for optimization of different environmental conditions for this isolated microalga.     

Neural network modeling in optimisation of continuous fermentation processes

The capability of self-recurrent neural networks in dynamic modeling of continuous fermentation is investigated in this simulation study. In the past, feedforward neural networks have been successfully used as one-step-ahead predictors. However, in steady-state optimisation of continuous fermentations the neural network model has to be iterated to predict many time steps ahead into the future in order to get steady-state values of the variables involved in objective cost function, and this iteration may result in increasing errors. Therefore, as an alternative to classical feedforward neural network trained by using backpropagation method, self-recurrent multilayer neural net trained by backpropagation through time method was chosen in order to improve accuracy of long-term predictions. Prediction capabilities of the resulting neural network model is tested by implementing this into the Integrated System Optimisation and Parameter Estimation (ISOPE) optimisation algorithm. Maximisation of cellular productivity of the baker's yeast continuous fermentation was used as the goal of the proposed optimising control problem. The training and prediction results of proposed neural network and performances of resulting optimisation structure are demonstrated.     

Applying neural networks as software sensors for enzyme engineering

The on-line control of enzyme-production processes is difficult, owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables. For example, intelligent methods to predict the end point of fermentation could be of great economic value. Computer-assisted control based on artificial-neural-network models offers a novel solution in such situations. Well-trained feedforward-backpropagation neural networks can be used as software sensors in enzyme-process control; their performance can be affected by a number of factors.     

On-line estimation of biomass concentration using a neural network and information about metabolic state

This paper deals with the design of a neural network-based biomass concentration estimation system. This system is enhanced by the incorporation of information about the actual metabolism of the microorganism cultivated, which is taken from an on-line knowledge-based system. Two different design approaches have been investigated using the fed-batch cultivation of bakers yeast as the model process. In the first, metabolic state (MS) data were passed as additional input to the neural network; in the second, these data were used to select a neural network suitable for the specific MS. Two neural network types—feed-forward (Levenberg-Marquardt) and cascade correlation—were applied to this system and tested, and the performances of these neural networks were compared.     

The simultaneous recurrent neural network for addressing the scaling problem in static optimization.

A trainable recurrent neural network, Simultaneous Recurrent Neural network, is proposed to address the scaling problem faced by neural network algorithms in static optimization. The proposed algorithm derives its computational power to address the scaling problem through its ability to "learn" compared to existing recurrent neural algorithms, which are not trainable. Recurrent backpropagation algorithm is employed to train the recurrent, relaxation-based neural network in order to associate fixed points of the network dynamics with locally optimal solutions of the static optimization problems. Performance of the algorithm is tested on the NP-hard Traveling Salesman Problem in the range of 100 to 600 cities. Simulation results indicate that the proposed algorithm is able to consistently locate high-quality solutions for all problem sizes tested. In other words, the proposed algorithm scales demonstrably well with the problem size with respect to quality of solutions and at the expense of increased computational cost for large problem sizes.     

生物量浓度实时在线检测方法的研究

微生物的存在会改变发酵液的电特性,发酵液在无线电频率范围内的电容率增量是测量频率和生物量浓度的函数.基于对发酵液电容率分布的研究,提出了测量生物量浓度的新方法.用此方法不用取样就能对发酵液中的生物量进行实时在线测量,而且测得的是活的生物量浓度.制作的电极直接插入发酵器中并满足高温蒸气灭菌条件.此方法在生化制药、食品发酵、啤酒酿造、污水检测等工业领域里有很好的推广应用前景.     

Neural networks applied to the prediction of fed-batch fermentation kinetics of Bacillus thuringiensis

This paper proposes using a new recurrent neural network model (RNNM) to predict and control fed batch fermentations of Bacillus thuringiensis. The control variables are the limiting substrate and the feeding conditions. The multi-input multi-output RNNM proposed has twelve inputs, seven outputs, nineteen neurons in the hidden layer, and global and local feedbacks. The weight update learning algorithm designed is a version of the well known backpropagation through time algorithm directed to the RNNM learning. The error approximation for the last epoch of learning is 2% and the total learning time is 51 epochs, where the size of an epoch is 162 iterations. The RNNM generalization was carried out reproducing a B. thuringiensis fermentation not included in the learning process. It attains an error approximation of 1.8%.     

Backpropagation neural network predictive control and control scheme comparison of 2,3-butanediol fermentation by Klebsiella oxytoca

A backpropagation neural network (BPN) was applied for the control study of 2,3-butanediol fermentation (2,3-BDL) carried by Klebsiella oxytoca. The measurements of cell mass and glucose were not included in the network models, instead, only the on-line measured product concentrations from the MIMS (membrane introduction mass spectrometer) were involved. Oxygen composition was chosen to be the control variable for this fermentation system for the formation of 2,3-BDL is regulated by oxygen. Oxygen composition was directly correlated to the measured product concentrations. A two-dimensional (number of input nodes by number of data sets) moving window to supply data for on-line, dynamic learning of this fermentation system was applied. The input nodes of the networks were also properly selected. Two neural network control schemes for this 2,3-BDL fermentation were discussed and compared in this work. Fermentations often exist time delay due to the measurement and their slow reaction nature. Hence, the order of time delay for the network controller was also investigated.     

On-line biomass estimation in biosurfactant production process by <Emphasis Type="Italic">Candida lipolytica</Emphasis> UCP 988

Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation of biomass concentration, in biosurfactant production by Candida lipolytica UCP 988, as a nonlinear function of pH and dissolved oxygen. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consists of one hidden layer with four neurons. The performance of the ANN was checked using experimental data. The results obtained indicate a very good predictive capacity for the ANN-based software sensor with values of R2 of 0.969 and RMSE of 0.021 for biomass concentration. Estimated biomass using the ANN was proved to be a simple, robust and accurate method.