Electrical conductivity as a state indicator for the start-up period of anaerobic fixed-bed reactors

Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors

The immediate response to the state disturbances of anaerobic digestion is essential to prevent anaerobic digestion failure. However, frequent monitoring of the state and performance of anaerobic digestion is challenging. Thus, deep learning models were investigated to predict the state and performance variables from online sensor data. The online sensor data, including pH, electric conductivity, and oxidation-reduction potential, were used as the input features to build deep learning models. The state and performance data measured offline were used as the labels. The model performance was compared for several deep learning models of convolutional neural network (CNN), long short-term memory (LSTM), dense layer, and their combinations. The combined model of CNN and bidirectional LSTM was robust and well-generalized in predicting the state and performance variables (R² = 0.978, root mean square error = 0.031). The combined model is an excellent soft sensor for monitoring the state and performance of anaerobic digestion from electrochemical sensors.

Substrate-to-inoculum ratio drives solid-state anaerobic digestion of unamended grape marc and cheese whey

Inoculation dose is a key operational parameter for the solid-state anaerobic digestion (SS-AD) of lignocellulosic biomass, maximum methane recovery, and stable digester performance. The novelty of this study was the co-digestion of unamended full-strength grape marc and cheese whey for peak methane extraction at variable inoculation levels. An acclimatised digestate from a preceding anaerobic treatment was used as a downstream inoculum. The impact of inoculum size (wet weight) was evaluated at 0/10, 5/5, 7/3 and 9/1 substrate-to-inoculum (S/I) ratios, corresponding to an initial concentration of 20-30% total solids (TS) in digesters over 58 days at 45°C. The optimal 7/3 S/I produced the highest cumulative methane yield, 6.45 L CH4 kg-1 VS, coinciding with the lowest initial salinity at 11%; the highest volumetric methane productivity rate of 0.289±0.044 L CH4 LWork-1 d-1; the highest average COD/N ratio of 9.88; the highest final pH of 9.13, and a maximum 15.07% elemental carbon removal; for a lag time of 9.4 days. This study identified an optimal inoculation dose and opens up an avenue for the direct co-digestion of grape marc and cheese whey without requirements for substrate pretreatment, thus improving the overall bioenergy profile of the winery and dairy joint resource recovery operations.

Electrical Conductivity for Monitoring the Expansion of the Support Material in an Anaerobic Biofilm Reactor

This article describes the use of the electrical conductivity for measuring bed expansion in a continuous anaerobic biofilm reactor in order to prevent the exit of support material from the reactor with the consequent loss of biomass. The substrate used for the tests is obtained from a two-stage anaerobic digestion (AD) process at the pilot scale that treats the liquid fraction of fruit and vegetable waste (FVW). Tests were performed with the raw substrate before anaerobic treatment (S1), the effluent from the hydrolysis reactor (S2), and the effluent from the methanogenic reactor (S3) to evaluate its effect on the electrical conductivity values and its interaction with colonized support material. The tests were carried out in a 32 L anaerobic inverse fluidized bed reactor (IFBR), which was inoculated with colonized support material and using two industrial electrodes at different column positions. The results with the previously digested samples (S2 and S3) were satisfactory to detect the presence of support material at the points where the electrodes were placed since the electrical conductivity values showed significant changes of up to 0.5 V, while with substrate S1 no significant voltage differences were appreciated. These results demonstrate that electrical conductivity can be used as an economic and simple mean for monitoring the support material expansion in order to avoid over expansion in the IFBR. It was also demonstrated that the conditions of the substrate in the methanogenic stage (pH and presence of volatile fatty acids) do not affect the operation of the electrical conductivity detection system.

Neural network-based software sensors for the estimation of key components in brewery wastewater anaerobic digester: an experimental validation

This work focused on the experimental validation of software sensors with a view to improving on-line anaerobic digester monitoring. Based on cheaply available measurements such as conductivity, temperature, pH, redox potential, total suspended solids concentration and digester inflows and outflows, an intelligent estimator was built to reproduce the evolutions of key components such as volatile fatty acid, carbonate and alkalinity concentrations, as well as biogas composition (methane and carbon dioxide). The proposed solution considers a principal component pre-processing of the data selected as inputs of a radial basis function neural network (RBF-ANN) structure, using a particular sequential learning algorithm. Process dynamics were also taken into account, introducing a moving horizon version of this network (MH-RBF-ANN). Experimental results demonstrated the capacity of the MH-RBF-ANN to correctly predict the key-component evolutions and to improve the estimation accuracy, compared to the classical RBF-ANN.

Fractal Analysis of pH Time-Series of an Anaerobic Digester for Cheese Whey Treatment

Cheese whey is a byproduct of the cheese industry and contains high concentrations of organic matter. Anaerobic digestion (AD) technology is an attractive solution to whey disposal since it allows the reduction of organic matter and simultaneously generates energy via biogas. The biological degradation of cheese whey is characterized by an unstable operation. A critical operational issue in the AD treatment of cheese whey is the tendency of rapid acidification of the waste requiring robust monitoring and control systems for reliable and efficient operation. Recent studies show that techniques based on fractal analysis of time series can be used for the indirect monitoring of critical variables of AD process (i. e., COD, VFA and methane production) for agro-industrial wastewaters. In this work, the application of the fractal analysis of pH time series obtained from an up-flow digester for cheese whey treatment is presented. The results suggest that fractal analysis can be applied to the indirect monitoring of a representative and high strength dairy wastewater. Furthermore, although the complex phenomena underlying in pH in the AD of cheese whey, the fractal analysis can unveil correlations of fractal parameters with key process variables.