Performance parameter prediction for sewage sludge digesters using reflectance FT-NIR spectroscopy

Agronomic characterization of anaerobic digestates with near-infrared spectroscopy

Anaerobic digestion is an increasingly widespread process for organic waste treatment and renewable energy production due to the methane content of the biogas. This biological process also produces a digestate (i.e., the remaining content of the waste after treatment) with a high fertilizing potential. The digestate composition is highly variable due to the various organic wastes used as feedstock, the different plant configurations, and the post-treatment processes used. In order to optimize digestate spreading on agricultural soils by optimizing the fertilizer dose and, thus, reducing environmental impacts associated to digestate application, the agronomic characterization of digestate is essential. This study investigates the use of near infrared spectroscopy for predicting the most important agronomic parameters from freeze-dried digestates. A data set of 193 digestates was created to calibrate partial least squares regression models predicting organic matter, total organic carbon, organic nitrogen, phosphorus, and potassium contents. The calibration range of the models were between 249.8 and 878.6 gOM.kgDM^-1, 171.9 and 499.5 gC.kgDM^-1, 5.3 and 74.1 gN.kgDM^-1, 2.7 and 44.9 gP.kgDM^-1 and between 0.5 and 171.8 gK.kgDM^-1, respectively. The calibrated models reliably predicted organic matter, total organic carbon, and phosphorus contents for the whole diversity of digestates with root mean square errors of prediction of 70.51 gOM.kgDM^-1, 34.84 gC.kgDM^-1 and 4.08 gP.kgDM^-1, respectively. On the other hand, the model prediction of the organic nitrogen content had a root mean square error of 7.55 gN.kgDM^-1 and was considered as acceptable. Lastly, the results did not demonstrate the feasibility of predicting the potassium content in digestates with near infrared spectroscopy. These results show that near infrared spectroscopy is a very promising analytical method for the characterization of the fertilizing value of digestates, which could provide large benefits in terms of analysis time and cost.

Qualitative analysis of activated sludge using FT-IR technique

The ability to measure and control the composition of activated sludge is an important issue, aiming at evaluating the effectiveness of changes occurring in the sludge, what determines its usefulness to treat wastewater. In this research, diffuse reflectance infrared Fourier transform (FTIR–DRIFT) technique was used, which relies on measuring the reflectance of the powdered substance’s surface layer and capturing spectra in range of infrared wave. First, spectra correlation table of the substances mostly occurring in wastewater was developed to assess the main components of the tested samples of activated sludge. The simplest compounds containing functional groups characteristic for particular chemical classes were chosen: peptides (peptone and albumin), fats (glycerin and fatty acids), carbohydrates (glucose and sucrose), nitrogen compounds (NaNO₃ and NH₄SO₄), sulfur compounds (Na₂SO₄ and Na₂S₂O₃), silicate, etc. The spectra of those substances were captured and characteristic absorption bands for respective bonds in the function groups were assigned. Second, samples of activated sludge from lab-scale membrane bioreactors (MBRs), which purifies petroleum wastewater, were taken. Samples were properly prepared (lyophilization and homogenization) and their spectra were captured. During spectra analysis, previously developed correlation table was used. In obtained spectra of activated sludge, absorption bonds characteristic for amides, peptides, carbohydrates, fats, and aliphatic was identified. The spectra profile of the sludge sample from MBR feed with petroleum wastewater was slightly different from the control MBR sample’s spectra. Intensity of bands in the area characteristic for aliphatic compounds and phenols was clearly higher. This study proves the usefulness of FT-IR technique to observe changes in the chemical composition of activated sludge.

Predicting aqueous solubility of environmentally relevant compounds from molecular features: a simple but highly effective four-dimensional model based on Project to Latent Structures

The aqueous solubility (log S) of xenobiotic chemicals has been identified as a key characteristic in determining their bioaccessibility/bioavailability and their fate and transport in aquatic environments. We here explore and evaluate the use of a state-of-the-art data analysis technique (Project to Latent Structures, PLS) to estimate log S of environmentally relevant chemicals. A large number (n = 624) of molecular descriptors was computed for over 1400 organic chemicals, and then refined by a feature selection technique. Candidate predictor descriptors were fitted to data by means of PLS, which was optimized by an internal leave-one-out cross-validation technique and validated by an external data set. The final (best) PLS model with only four variables (AlogP, X1sol, Mv, and E) exhibited noteworthy stability and good predictive power. It was able to explain 91% of the data (n = 1400) variance with an average absolute error of 0.5 log units through the solubilities span over 12 orders of magnitude. The newly proposed model is transparent, easily portable from one user to another, and robust enough to accurately estimate log S of a wide range of emerging contaminants.

Integration of NIRS and PCA techniques for the process monitoring of a sewage sludge anaerobic digester

This study investigates the use of Hotelling's T(2) control charts as the basis of a process monitor for sewage sludge anaerobic digestion. Fourier transform near infrared spectroscopy was used to produce partial least squares regression models of volatile fatty acids, bicarbonate alkalinity and volatile solids. These were utilised in a series of principle component analysis models along with spectral data from digestate and feedstock samples to produce a pseudo steady state model, which was then used with an independent test set to evaluate the system. The system was able to identify disturbances to the digester due to a temporary alteration of the type of feedstock to the digester and separately, halving of the hydraulic retention time of the digester. It could also provide advance warning of disturbances to the digester. This technique could be used to improve the performance of sewage sludge anaerobic digesters by enabling optimisation of the process.

Multivariate statistically-based modelling of a membrane bioreactor for wastewater treatment using 2D fluorescence monitoring data

This work presents the development of multivariate statistically-based models for monitoring several key performance parameters of membrane bioreactors (MBR) for wastewater treatment. This non-mechanistic approach enabled the deconvolution of 2D fluorescence spectroscopy data, a powerful technique that has previously been shown to capture important information regarding MBR performance. Projection to latent structure (PLS) modelling was used to integrate 2D fluorescence data, after compression through parallel factor analysis (PARAFAC), with operation and analytical data to describe an MBR fouling indicator (transmembrane pressure, TMP), five descriptors of the effluent quality (total COD, soluble COD, concentration of nitrite and nitrate, total nitrogen and total phosphorus in the permeate) and the biomass concentration in the bioreactor (MLSS). A multilinear correlation was successfully established for TMP, CODtp and CODsp, whereas the optimised models for the remaining outputs included quadratic and interaction terms of the compressed 2D fluorescence matrices. Additionally, the coefficients of the optimised models revealed important contributions of some of the input parameters to the modelled outputs. This work demonstrates the applicability of 2D fluorescence and statistically-based models to simultaneously monitor multiple key MBR performance parameters with minimal analytical effort. This is a promising approach to facilitate the implementation of MBR technology for wastewater treatment.