Software Sensor for Online Estimation of the VFA’s Concentration in Anaerobic Digestion Processes via a High-Order Sliding Mode Observer

Development of a low-cost electrochemical sensor for monitoring components in wastewater treatment processes

Anaerobic digestion (AD) is a complex biological process widely used to decompose various types of organic matter, as well as to produce some metabolites and biogas. Diverse microorganism groups cooperate in many intricate metabolic routes so that organic matter can be degraded. However, any imbalance on these routes can lead to process instability or even failure. Therefore, a proper monitoring system, as well as a good understanding of the process, are key steps to improve performance and stability. Several mathematical models have been developed to represent AD. Despite this, process monitoring is mostly conducted by analytical methods, whose equipment is either expensive or the analyses are time-consuming, which may be a hindrance to low-budget developments. The objective of this study was to develop a low-cost electrochemical sensor to monitor components in wastewater treatment plants. Hundreds of synthetically supplemented sugarcane vinasse and synthetic domestic sewage samples were characterised. The obtained signals were used to calibrate principal component regression, partial least square and artificial neural network estimation models. The predictable variables were chemical oxygen demand, volatile fatty acids, sodium bicarbonate, beef extract, and lipids, and their R2 ranged from 0.84 to 0.99, depending on the component.

Diagnosis and Monitoring of Volatile Fatty Acids Production from Raw Cheese Whey by Multiscale Time-Series Analysis

Anaerobic treatment is a viable alternative for the treatment of agro-industrial waste. Anaerobic digestion reduces organic load and produces volatile fatty acids (VFA), which are precursors of value-added products such as methane-rich biogas, biohydrogen, and biopolymers. Nowadays, there are no low-cost diagnosis and monitoring systems that analyze the dynamic behavior of key variables in real time, representing a significant limitation for its practical implementation. In this work, the feasibility of using the multiscale analysis to diagnose and monitor the key variables in VFA production by anaerobic treatment of raw cheese whey is presented. First, experiments were carried out to evaluate the performance of the proposed methodology under different operating conditions. Then, experimental pH time series were analyzed using rescaled range (R/S) techniques. Time-series analysis shows that the anaerobic VFA production exhibits a multiscale behavior, identifying three characteristic regions (i.e., three values of Hurst exponent). In addition, the dynamic Hurst exponents show satisfactory correlations with the chemical oxygen demand (COD) consumption and VFA production. The multiscale analysis of pH time series is easy to implement and inexpensive. Hence, it could be used as a diagnosis and indirect monitoring system of key variables in the anaerobic treatment of raw cheese whey.

Model-Based Monitoring of Biotechnological Processes—A Review

The monitoring of the main variables and parameters of biotechnological processes is of key importance for the research and control of the processes, especially in industrial installations, where there is a limited number of measurements. For this reason, many researchers are focusing their efforts on developing appropriate algorithms (software sensors (SS)) to provide reliable information on unmeasurable variables and parameters, based on the available on-line information. In the literature, a large number of developments related to this topic that concern data-based and model-based sensors are presented. Up-to-date reviews of data-driven SS for biotechnological processes have already been presented in the scientific literature. Hybrid software sensors as a combination between the abovementioned ones are under development. This gives a reason for the article to be focused on a review of model-based software sensors for biotechnological processes. The most applied model-based methods for monitoring the kinetics and state variables of these processes are analyzed and compared. The following software sensors are considered: Kalman filters, methods based on estimators and observers of a deterministic type, probability observers, high-gain observers, sliding mode observers, adaptive observers, etc. The comparison is made in terms of their stability and number of tuning parameters. Particular attention is paid to the approach of the general dynamic model. The main characteristics of the classic variant proposed by D. Dochain are summarized. Results related to the development of this approach are analyzed. A key point is the presentation of new formalizations of kinetics and the design of new algorithms for its estimation in cases of uncertainty. The efficiency and applicability of the considered software sensors are discussed.

Indirect Monitoring of Anaerobic Digestion for Cheese Whey Treatment

Efficient monitoring is an open problem in the operation of anaerobic digestion processes, due to the lack of accurate, low-cost, and proper sensors for the on-line monitoring of key process variables. This paper presents two approaches for the indirect monitoring of the anaerobic digestion of cheese whey wastewater. First, the observability property is addressed using conventional and nonconventional techniques, including an observability index. Then, two model-based observer techniques, an extended Luenberger observer, a sliding mode observer, and a data-driven technique based on fractal analysis are formulated and discussed. The performance and capabilities of the proposed methodologies are illustrated on a validated model with experimental data of the anaerobic digestion of cheese whey. Experimental pH measurements are used for the data-driven approach based on fractal analysis. The experimental data sets correspond to experimental conditions (pH > 7.5 and temperature (T) = 40 °C) favoring volatile fatty acid (VFA) production (30 g/L) with simultaneously acceptable biogas production (3420 mL). Results also show that the proposed observers were able to predict satisfactory key process variables. On the other hand, the fractal analysis provides reliable qualitative trends of VFA production and chemical oxygen demand (COD) consumption.