Acceptor properties of "carbon nanotubes-redox-active polymer based on bovine serum albumin modified with ferrocenecarboxaldehyde" composite for creating a BOD biosensor with Blastobotrys adeninivorans BKM Y-2677 yeast

The possibility of using a composite material based on bovine serum albumin (BSA) covalently bonded with ferrocenecarboxaldehyde and containing carbon nanotubes (CNT) for the immobilization of Blastobotrys adeninivorans BKM Y-2677 (B. adeninivorans) yeast is discussed. The optimal ratio of ferrocenecarboxaldehyde to BSA for the redox-active polymer synthesis is 1:2, since the heterogeneous electron transfer constant is 0.45 ± 0.01 s-1. When carbon nanotubes (CNTs) are added to this polymer, the heterogeneous electron transfer constant increases: at a CNT specific density of 2.5 µg/mm2, it reaches a maximum value of 0.55 ± 0.01 s-1. The addition of CNTs into the conducting system leads to increasing of the rate constant of interaction redox species with B. adeninivorans yeast by an order: the rate constant of interaction between B. adeninivorans yeast and electroactive particles in a redox-active polymer is 0.056 ± 0.005 dm3/g × s and in a composite material based on CNTs is 0.51 ± 0.02 dm3/g × s. The yeast specific density at the electrode of 0.1 mg/mm2 and electrolyte pH of 6.2 was chosen as the working value for the receptor system operation. Immobilized in a composite material, yeast oxidizes a wider range of substrates compared with a similar receptor element based on the ferrocene mediator. The biosensors formed on the basis of hybrid polymers have a high sensitivity with a lower limit of determined concentrations of 1.5 mg/dm3 with an assay time of 5 min and a high correlation (R = 0.9945) with the results of the standard method for determining biochemical oxygen demand (BOD) in nine real surface water samples of the Tula region.

Development of a wide-range soft sensor for predicting wastewater BOD5 using an eXtreme gradient boosting (XGBoost) machine

In wastewater monitoring, detecting extremely high pollutant concentrations is necessary to properly calibrate the treatment process. However, existing hardware sensors have a limited linear range which may fail to measure extremely high levels of pollutants; and likewise, the conventional "soft" model sensors are not suitable for the highly-skewed data distributions either. This study developed a new soft sensor by using eXtreme Gradient Boosting (XGBoost) machine learning to 'measure' the wastewater organics (in terms of 5-day biochemical oxygen demand (BOD5)). The soft sensor was tested on influent and effluent BOD5 of two different wastewater treatment plants to validate the results. The model results showed that XGBoost can detect these extreme values better than conventional soft sensors. This new soft sensor can function using a sparse input matrix via XGBoost's sparsity awareness algorithm - which can address the limitation of the conventional soft sensor with the fallibility of supporting hardware sensors even.

Real-time biomonitoring of oxygen uptake rate and biochemical oxygen demand using a novel optical biogas respirometric system

In response to the ever-increasing need for monitoring-based process control of wastewater treatment plants, an online applicable respirometer shows great promise for real-time measurement of oxygen uptake rate (OUR) and biochemical oxygen demand (BOD) measurements as a surrogate of the biodegradability of wastewater. Here, we have developed a photosensor-assisted real-time respirometric system equipped with bubble counting sensors for accurate measurement of microbial oxygen consumption in a bottle. This system can measure OUR and BOD in a bottle equipped with a tube containing NaOH solution to absorb carbon dioxide and supplied with continuous atmospheric oxygen to the bottle, which reliably supplies non-limiting dissolved oxygen (DO) for aerobic biodegradation even at high organic loads. These technical improvements allow a sensitive and rapid analytical tool offering real-time profiles of oxygen uptake rate as well as BOD measurements with an extended measurable range (0-420 mg O₂/L), enabling significant reduction or elimination of dilution steps. The respirometric system was used to elucidate the biodegradable kinetics of domestic and swine wastewaters as a function of the type and concentration of organic matters, depending on source characteristics including rapidly or slowly oxidizable organic substances by bacteria. Compared with conventional and manometric BOD methods, our method is reliable and accurate.

Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology

Biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS) and total suspended solids (TSS) are the most commonly regulated wastewater effluent parameters. The measurement and prediction of these parameters are essential for assessing the performance and upgrade of wastewater treatment facilities. In this study, a new methodology, combining a linear stochastic model (ARIMA) and nonlinear outlier robust extreme learning machine technique (ORELM) with various preprocesses, is presented to model the quality parameters of effluent wastewater (ARIMA-ORELM). For each of the studied parameters, 144 different (144 × 8 models) linear models (ARIMA) are presented, with the superior model of each parameter being selected based on statistical indices. Moreover, 48 nonlinear models (ORELM) and 48 hybrid models (ARIMA-ORELM) were considered. The use of linear and nonlinear approaches to model the linear and nonlinear terms (respectively) of each time series in the hybrid model increased the efficiency and accuracy of the predictions for all of the time series. The influent wastewater nonlinear TSS model and the effluent COD and BOD models attained the best performance with a high correlation coefficient of 0.95. The use of hybrid models improved the prediction capability of all quality parameters with the best performance being achieved for the effluent BOD model (R² = 0.99).

Predicting influent biochemical oxygen demand: Balancing energy demand and risk management

Ready access to comprehensive influent information can help water reclamation plant (WRP) operators implement better real-time process controls, provide operational reliability and reduce energy consumption. The five-day biochemical oxygen demand (BOD₅), a critical parameter for WRP process control, is expensive and difficult to measure using hard-sensors. An alternative approach based on a soft-sensor methodology shows promise, but can be problematic when used to predict high BOD₅ values. Underestimating high BOD₅ concentrations for process control could result in an insufficient amount of aeration, increasing the risk of an effluent violation. To address this issue, we tested a hierarchical hybrid soft-sensor approach involving multiple linear regression, artificial neural networks (ANN), and compromise programming. While this hybrid approach results in a slight decrease in overall prediction accuracy relative to the approach based on ANN only, the underestimation percentage is substantially lower (37% vs. 61%) for predictions of carbonaceous BOD₅ (CBOD₅) concentrations higher than the long-term average value. The hybrid approach is also flexible and can be adjusted depending on the relative importance between energy savings and managing the risk of an effluent violation.

A simple model for estimating the concentrations of natural estrogens in raw wastewater

This study provides a tool for predicting the concentrations of the natural estrogens (NEs) estrone, 17β-estradiol and estriol in raw wastewater (WW). Data characterizing the biochemical oxygen demand (BOD), NE concentrations, and discharges of raw sewage to wastewater treatment plants (WWTPs) were collected from various publications and used in the model formulation. A strong correlation was found between the log transformed BOD and the log transformed estrone load (r²=0.84, n=61), the log transformed 17β-estradiol load (r²=0.89, n=52) and the log transformed estriol load (r²=0.80, n=40). The models are reasonably accurate when compared to the measured concentrations and slightly better than previous modeling efforts. The relative amounts of data falling within ±50% error were 67% for estrone, 63% for 17β-estradiol, and 55% for estriol. Because the model was developed from a wide array of WWTPs from five continents, it is universal and can be used for projecting concentrations of NEs from a wide range of mixed domestic and industrial sources, but may be less precise when sources contain high levels of NEs or BOD (e.g., WW from dairy farms and food processing plants). The model is expected to improve our ability to predict the fate of NEs in WWTPs and in the receiving environment, which currently relies on estimating the concentrations of NEs in raw wastewater. Its application is especially valuable since direct measurement of NEs in raw WW is expensive and practically impossible in many developing countries due to the lack of expertise and funds.

A yeast co-culture-based biosensor for determination of waste water contamination levels

Artificial microbial co-cultures were formed to develop the receptor element of a biosensor for assessment of biological oxygen demand (BOD). The co-cultures possessed broad substrate specificities and enabled assays of water and fermentation products within a broad BOD range (2.4-80 mg/dm(3)) with a high correlation to the standard method (R = 0.9988). The use of the co-cultures of the yeasts Pichia angusta, Arxula adeninivorans and Debaryomyces hansenii immobilized in N-vinylpyrrolidone-modified poly(vinyl alcohol) enabled developing a BOD biosensor possessing the characteristics not inferior to those in the known biosensors. The results are indicative of a potential of using these co-cultures as the receptor element base in prototype models of instruments for broad application.