Rapid characterization of sulfur and phosphorus in organic waste by near infrared spectroscopy

Near-infrared spectroscopy (NIRS) has recently emerged as a valuable tool for monitoring organic waste utilized in anaerobic digestion processes. Over the past decade, NIRS has significantly improved the characterization of organic waste by enabling the prediction of several crucial parameters such as biochemical methane potential, carbohydrate, lipid and nitrogen contents, Chemical Oxygen Demand, and kinetic parameters. This study investigates the application of NIRS for predicting the levels of Sulfur (S) and Phosphorus (P) within organic waste materials. The results for sulfur prediction exhibited a high level of accuracy, yielding an error of 1.21 g/Kg[TS] in an independently validated dataset, coupled with an R-squared value of 0.84. Conversely, the prediction of phosphorus proved to be slightly less successful, showing an error of 1.49 g/Kg[TS] with an R-squared value of 0.70. Furthermore, the disparities in performance seem to stem from the inherent correlation between the spectral data and the sulfur or phosphorus contents. Significantly, a variable selection technique known as CovSel was employed, shedding light on the differing approaches used for sulfur and phosphorus predictions. In the case of sulfur, the prediction was achieved through a direct correlation with wavelengths associated with sulfur-related functional groups (such as R - S(=O)2 - OH, -SH, and R-S-S-R) present in the NIR spectra. In contrast, phosphorus prediction relied on an indirect correlation with absorption bands related to organic matter (including CH, CH2, CH3, -CHO, R-OH, C = O, -CO2H, and CONH).

Determination of the optimal feed recipe of anaerobic digesters using a mathematical model and a genetic algorithm

Recently, numerous experimental studies have been undertaken to understand the interactions between different feedstocks in anaerobic digestion. They have unveiled the potential of blending substrates in the process. Nevertheless, these experiments are time-intensive, prompting the exploration of various optimization approaches. Notably, genetic algorithms have gained interest due to their population-based structures allowing them to efficiently yield multiple Pareto-optimal solutions in a single run. This study uses a simplified static anaerobic co-digestion model as the fitness function for a multi-objective optimization. The optimization aims to achieve a methane production set-point while reducing the output ammonia nitrogen and increasing the recipe' profitability. Thus, the study employs genetic algorithms to identify Pareto fronts and constraints confined the solution space within feasible boundaries. It also underscores the influence of economic considerations on the viable solution space. Ultimately, the optimal feed recipe not only ensures stable operations within the digester but also enhances associated profits.

Fast and robust NIRS-based characterization of raw organic waste: Using non-linear methods to handle water effects

Fast characterization of organic waste using near infrared spectroscopy (NIRS) has been successfully developed in the last decade. However, up to now, an on-site use of this technology has been hindered by necessary sample preparation steps (freeze-drying and grinding) to avoid important water effects on NIRS. Recent research studies have shown that these effects are highly non-linear and relate both to the biochemical and physical properties of samples. To account for these complex effects, the current study compares the use of many different types of non-linear methods such as partial least squares regression (PLSR) based methods (global, clustered and local versions of PLSR), machine learning methods (support vector machines, regression trees and ensemble methods) and deep learning methods (artificial and convolutional neural networks). On an independent test data set, non-linear methods showed errors 28% lower than linear methods. The standard errors of prediction obtained for the prediction of total solids content (TS%), chemical oxygen demand (COD) and biochemical methane potential (BMP) were respectively 8%, 160 mg(O₂).gTS^-1 and 92 mL(CH₄).gTS^-1_. These latter errors are similar to successful NIRS applications developed on freeze-dried samples. These findings hold great promises regarding the development of at-site and online NIRS solutions in anaerobic digestion plants.

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.

On-site substrate characterization in the anaerobic digestion context: A dataset of near infrared spectra acquired with four different optical systems on freeze-dried and ground organic waste

The near infrared spectra of thirty-three freeze-dried and ground organic waste samples of various biochemical composition were collected on four different optical systems, including a laboratory spectrometer, a transportable spectrometer with two measurement configurations (an immersed probe, and a polarized light system) and a micro-spectrometer. The provided data contains one file per spectroscopic system including the reflectance or absorbance spectra with the corresponding sample name and wavelengths. A reference data file containing carbohydrates, lipid and nitrogen content, biochemical methane potential (BMP) and chemical oxygen demand (COD) for each sample is also provided. This data enables the comparison of the optical systems for predictive model calibration based for example on Partial Least Squares Regression (PLS-R) [1], but could be used more broadly to test new chemometrics methods. For example, the data could be used to evaluate different transfer functions between spectroscopic systems [2]. This dataset enabled the research work reported by Mallet et al. 2021 [3].

Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations

Fast characterization of solid organic waste using near infrared spectroscopy has been successfully developed in the last decade. However, its adoption in biogas plants for monitoring the feeding substrates remains limited due to the lack of applicability and high costs. Recent evolutions in the technology have given rise to both more compact and more modular low-cost near infrared systems which could allow a larger scale deployment. The current study investigates the relevance of these new systems by evaluating four different Fourier transform near-infrared spectroscopic systems with different compactness (laboratory, portable, micro spectrometer) but also different measurement configurations (polarized light, at distance, in contact). Though the conventional laboratory spectrometer showed the best performance on the various biochemical parameters tested (carbohydrates, lipids, nitrogen, chemical oxygen demand, biochemical methane potential), the compact systems provided very close results. Prediction of the biochemical methane potential was possible using a low-cost micro spectrometer with an independent validation set error of only 91 NmL(CH₄).gTS^-1 compared to 60 NmL(CH₄).gTS^-1 for a laboratory spectrometer. The differences in performance were shown to result mainly from poorer spectral sampling; and not from instrument characteristics such as spectral resolution. Regarding the measurement configurations, none of the evaluated systems allowed a significant gain in robustness. In particular, the polarized light system provided better results when using its multi-scattered signal which brings further evidence of the importance of physical light-scattering properties in the success of models built on solid organic waste.

Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law

In near-infrared spectroscopy (NIRS), the linear relationship between absorbance and an absorbing compound concentration has been strictly defined by the Bouguer-Beer-Lambert law only for the case of transmission measurements of nonscattering media. However, various quantitative calibrations have been successfully built both on reflectance measurements and for scattering media. Although the lack of linearity for scattering media has been observed experimentally, the sound multivariate statistics and signal processing involved in chemometrics have allowed us to overcome this problem in most cases. However, in the case of samples with varying water content, important modifications of scattering levels still make calibrations difficult to build due to nonlinearities. Moreover, even when calibration procedures are successfully developed, many preprocessing methods used do not guarantee correct spectroscopic assignments (in the sense of a pure chemical absorbance). In particular, this may prevent correct modeling and interpretation of the structure of water. In this study, dynamic near-infrared spectra acquired during a drying process allow the study of the physical effects of water content variations, with a focus on the first overtone OH absorbance region. A model sample consisting of aluminum pellets mixed with water allowed us to study this specifically, without any other absorbing interaction terms related to the dry mass-absorbing constituents. A new formulation of the Bouguer-Beer-Lambert law is proposed, by expressing path length as a power function of water content. Through this new formulation, it is shown that a better and simpler prediction model of water content may be developed, with more precise and accurate identification of water absorbance bands.

Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics

In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water's interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects.

Comprehensive determination of input state variables dataset required for anaerobic digestion modelling (ADM1) based on characterisation of organic substrates

This article contains the data of 11 organic substrates including physicochemical, biochemical and nutritional characterisations. Additionally, it includes for all substrates the data of organic matter fractionation into easily biodegradable, slowly biodegradable and inert fractions performed with anaerobic respirometry method. Finally, based on physicochemical characterisations and organic matter fractionation, a detailed methodology for the determination of input state variables required for the anaerobic digestion model N°1 (ADM1) was presented and the dataset for all substrates is provided. An example of calculation for one substrate illustrates the methodology for the determination of these variables. Data provided in this article could be useful to any person interested in modelling anaerobic digestion and particularly co-digestion. Data could be also used for implementation of a database for anaerobic digestion modelling.

Modelling hydrolysis: Simultaneous versus sequential biodegradation of the hydrolysable fractions

Hydrolysis is considered the limiting step during solid waste anaerobic digestion (including co-digestion of sludge and biosolids). Mechanisms of hydrolysis are mechanistically not well understood with detrimental impact on model predictive capability. The common approach to multiple substrates is to consider simultaneous degradation of the substrates. This may not have the capacity to separate the different kinetics. Sequential degradation of substrates is theoretically supported by microbial capacity and the composite nature of substrates (bioaccessibility concept). However, this has not been experimentally assessed. Sequential chemical fractionation has been successfully used to define inputs for an anaerobic digestion model. In this paper, sequential extractions of organic substrates were evaluated in order to compare both models. By removing each fraction (from the most accessible to the least accessible fraction) from three different substrates, anaerobic incubation tests showed that for physically structured substrates, such as activated sludge and wheat straw, sequential approach could better describe experimental results, while this was less important for homogeneous materials such as pulped fruit. Following this, anaerobic incubation tests were performed on five substrates. Cumulative methane production was modelled by the simultaneous and sequential approaches. Results showed that the sequential model could fit the experimental data for all the substrates whereas simultaneous model did not work for some substrates.

Fast ADM1 implementation for the optimization of feeding strategy using near infrared spectroscopy

Optimization of feeding strategy is an essential issue of anaerobic co-digestion that can be greatly assisted with simulation tools such as the Anaerobic Digestion Model 1. Using this model, a set of parameters, such as the biochemical composition of the waste to be digested, its methane production yield and kinetics, has to be defined for each new substrate. In the recent years, near infrared analyses have been reported as a fast and accurate solution for the estimation of methane production yield and biochemical composition. However, the estimation of methane production kinetics requires time-consuming analysis. Here, a partial least square regression model was developed for a fast and efficient estimation of methane production kinetics using near infrared spectroscopy on 275 bio-waste samples. The development of this characterization reduces the time of analysis from 30 days to a matter of minutes. Then, biochemical composition and methane production yield and kinetics predicted by near infrared spectroscopy were implemented in a modified Anaerobic Digestion Model n°1 in order to simulate the performance of anaerobic digestion processes. This approach was validated using different data sets and was demonstrated to provide a powerful predictive tool for advanced control of anaerobic digestion plants and feeding strategy optimization.

Fast characterization of solid organic waste content with near infrared spectroscopy in anaerobic digestion

The development of anaerobic digestion involves both co-digestion of solid wastes and optimization of the feeding recipe. Within this context, substrate characterisation is an essential issue. Although it is widely used, the biochemical methane potential is not sufficient to optimize the operation of anaerobic digestion plants. Indeed the biochemical composition in carbohydrates, lipids, proteins and the chemical oxygen demand of the inputs are key parameters for the optimisation of process performances. Here we used near infrared spectroscopy as a robust and less-time consuming tool to predict the solid waste content in carbohydrates, lipids and nitrogen, and the chemical oxygen demand. We built a Partial Least Square regression model with 295 samples and validated it with an independent set of 46 samples across a wide range of solid wastes found in anaerobic digestion units. The standard errors of cross-validation were 90mgO₂⋅gTS^-1 carbohydrates, 2.5∗10^-2g⋅gTS^-1 lipids, 7.2∗10^-3g⋅gTS^-1 nitrogen and 99mgO₂⋅gTS^-1 chemical oxygen demand. The standard errors of prediction were 53mgO₂⋅gTS^-1 carbohydrates, 3.2∗10^-2g⋅gTS^-1 lipids, 8.6∗10^-3g⋅gTS^-1 nitrogen and 83mgO₂⋅gTS^-1 chemical oxygen demand. These results show that near infrared spectroscopy is a new fast and cost-efficient way to characterize solid wastes content and improve their anaerobic digestion monitoring.

Combining pH and electrical conductivity measurements to improve titrimetric methods to determine ammonia nitrogen, volatile fatty acids and inorganic carbon concentrations

Volatile fatty acids (VFA), inorganic carbon (IC) and total ammonia nitrogen (TAN) are key variables in the current context of anaerobic digestion (AD). Accurate measurements like gas chromatography and infrared spectrometry have been developed to follow the concentration of these compounds but none of these methods are affordable for small AD units. Only titration methods answer the need for small plant monitoring. The existing methods accuracy was assessed in this study and reveals a lack of accuracy and robustness to control AD plants. To solve these issues, a new titrimetric device to estimate the VFA, IC and TAN concentrations with an improved accuracy was developed. This device named SNAC (System of titration for total ammonia Nitrogen, volatile fatty Acids and inorganic Carbon) has been developed combining the measurement of electrical conductivity and pH. SNAC were tested on 24 different plant samples in a range of 0-0.16 mol.L(-1) TAN, 0.01-0.21 mol.L(-1) IC and 0-0.04 mol.L(-1) VFA. The standard error was about 0.012 mol.L(-1) TAN, 0.015 mol.L(-1) IC and 0.003 mol.L(-1) VFA. The coefficient of determination R(2) between the estimated and reference data was 0.95, 0.94 and 0.95 for TAN, IC and VFA respectively. Using the same data, current methods based on key pH points lead to standard error more than 14.5 times higher on VFA and more than 1.2 times higher on IC. These results show that SNAC is an accurate tool to improve the management of AD plant.