Energy security is a growing societal and industrial concern that leads research and development toward more sustainable options. Biogas, a bio-alternative to conventional fuels, is a product generated from the anaerobic digestion of organic matter. This source of fuel production is more environmentally friendly compared to traditional fossil fuels, leading to a lower carbon footprint, higher air quality, and the promotion of a circular economy. Impurities of raw biogas, such as carbon dioxide, hydrogen sulfide, and other trace contaminants, make biogas conditioning necessary for most applications. In addition, biogas upgrading, technologies furthering biogas purity, is an important factor in the production of biomethane, a sustainable biofuel known more commonly as renewable natural gas (RNG). Diversifying fuel sources and providing energy sustainability while mitigating negative environmental effects makes RNG an attractive alternative to conventional natural gas. This document, Part I, provides an overview of current technologies related to biogas conditioning, such as sorption, oxidation, and biological treatments aimed at the removal of a wide variety of contaminants. Processes developed for biogas upgrading are also discussed, including physical/chemical absorption, pressure swing adsorption, and membrane separation. The focus of upgrading applies approaches in meeting a higher quality biofuel by further carbon dioxide exclusion to ease pipeline transport and increase combustion efficiency. These technologies present the core foundation of processes in the production of RNG; however, all face inherent challenges that deem further research and development a requirement for global adoption. The biggest challenges are either in the cost of reaching higher purities or the inability to do so without other operations. Thus, in conjunction with this document, emerging and developing technologies are provided in a separate analysis deemed Part II. Together, these documents offer a comprehensive understanding of current practices and growing technological developments.

In recent years, wood-based composites bonded with adhesives of conventional resins containing formaldehyde have raised concerns as corporations are becoming environmentally conscious and fossil fuels are in high demand. A new generation of adhesives are being developed from natural renewable biomass resources with considerable protein concentrations. In this study, wastewater treatment plant-activated sludge and biosolids are formulated into bio-adhesives via protein denaturation. Three urban-based biomass sources, consisting of waste-activated sludge, biosolids, and dewatered biosolids, were used to produce adhesives via alkaline denaturation of the present proteins. The mechanical properties of the produced adhesive were evaluated via lap shear testing. A fiberboard material was also generated using the formulated adhesives to analyze the practical application of the adhesives as binding materials. It was found that the waste adhesives possess significant bonding strength in relation to single lap shear testing, for each of the waste sludge feedstocks resulting in adhesive strengths nearing 700-1200 kPa which signifies potential as an applicable protein-based adhesive. Through small soy additions, the waste sludge adhesive formulations neared 2500 kPa detailing a lessened environmental burden of crops utilized for protein-based adhesive formulations. The prepared fiberboard also contained flexural mechanical properties in comparison to soy-based formulations, resulting in an average modulus of elasticity of 187 MPa, an average modulus of rupture of 5.6 MPa, and an average internal bond strength of 39.6 kPa. Although strength was sensitive to denaturant concentration as well as sample location, the resultant fiberboard has the potential for further application.

Renewable natural gas is an innovative alternative fuel source that has the potential to integrate seamlessly into the current energy and fuel sector. In addition, growing concerns related to energy security and environmental impact are incentivizing the development of RNG technologies. In conjunction with this document, current technologies related to biogas conditioning and biogas upgrading were covered in a separate analysis deemed Part I. With the current technologies, however, issues such as compositional quality, combustion efficiency, and high operational costs still need to be addressed before RNG can reach its true capability in use. Recent innovations have focused on optimizing techniques and introducing new methods to maximize methane yield and purity while minimizing costs and energy consumption. This document, Part II, provides an overview of emerging technologies related to further biogas upgrading, such as cryogenics, methane enrichment, and hybrid treatments, aimed at increasing cleaned biogas purity. Processes in development are also discussed, including industrial lung, supersonic separation, chemical hydrogenation, hydrate formation, and various biological treatments. The benefits of these advancements are increased purity for the ability to pipeline renewable natural gas in existing infrastructure, help industries reach sustainability goals, and contribute to a more resilient energy system. Together, Parts I and II offer a comprehensive understanding of both current and future technological developments.

Adhesive formulations derived from sustainable feedstocks, like waste-activated sludge and biosolids from wastewater treatment plants, are developed due to protein-based adhesives receiving attention for their low-cost, resourcefulness, and minimal ecological footprint. The protein composition and associated dynamic changes of the adhesive formulations were studied via gel permeation chromatography, which detailed a molecular size distribution of 8.72 × 105 g/mol for the adhesive formulation and 6.89 × 103 g/mol for the dewatered biosolid base fraction, which confirms the formation of multiple protein functional groups combining to form the larger adhesive molecules. Further analysis determined the types of proteins present in the dewatered biosolids as glutelin, prolamin, globulin, and albumin proteins, with the glutelin proteins as the most prevalent, as thus likely responsible for adhesive formation. The rheological properties of the novel protein adhesive were also studied to interpret the structure of the adhesives, which detailed the findings of viscoelastic properties and flow behaviors of each adhesive in relation to the wastewater treatment plant sample location, which yielded higher flow points, storage moduli, and loss moduli for the dewatered biosolids in comparison to the waste-activated sludge and biosolid adhesives, which correlates with the higher solids content of the dewatered biosolids and potentially cell rupturing when exposed to filtration stress.

An increase in the generation of waste within cities is unavoidable due to the increasing global population growth, particularly in urban areas. Municipal wastewater treatment plants (WWTPs) in these urban areas are being pushed to their design limits resulting in issues with WWTP residual management. This paper reviews potential applications of transitioning a municipal WWTP into an urban biorefinery for converting wastes into various value-added chemicals and energy. Primary WWTP-based residuals produced are waste-activated sludge, biosolids, grit, and effluent. These components are becoming viable feedstocks for producing many potential products and can be recovered for commercial purposes as opposed to simple disposal. Example products include chemicals, energy, and transportable biofuels. An advantage to biorefinery operations composed of WWTPs is that they provide greener solutions while posing little to no threat to the environment. There has also been an increasing interest in co-feedstocks to WWTPs, such as municipal solids, food wastes, agriculture wastes, and lignocellulosic biomass, which can enhance product yields while providing sustainable management solutions to these additional waste streams. Municipal wastewater influents generated within the USA have a chemical energy potential of 1.3 MJ/person/day which represents about 4

This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein-ligand blind-docking learning and prediction. Concerns about the chemical pathways and accumulation of PFAs in the environment and eventually in the human body has been rising due to empirical findings that levels of PFAs in human blood has been rising. DiffDock may offer a fast approach in determining the fate and potential molecular pathways of PFAs in human body.

The International Space Station (ISS) is the largest and the most isolated extraterrestrial environment that humans have lived in for extended periods of time. Currently, the Life Support System (LSS) within the ISS, which involves physicochemical processes, can only recycle a portion of water, and regain a fraction of O2 needed by the crew. The unsustainability and the inability of the current LSS to provide enough food require regular resupply missions from Earth making it unsuitable for deep space explorations (e.g., crewed Mars mission) due to limited payload capacity of current rocket designs. Thus, LSS of completely independent isolated environments needs to be bioregenerative (i.e., Bioregenerative Life Support System – BLSS), which is characterized by “closing the loop” in regeneration of air, food, water, and other materials that support human life using biological systems or processes (particularly those that involve autotrophs). This closed loop LSS requires an initial supply of resources and then recovers useful resources from wastes, thereby reducing dependence on resupply. Long term crew survival in space will rely mainly on the ability of the biological systems to provide most of the needed life support materials, with physicochemical serving only as a fail-safe or for redundancy. In this review, the state-of-the-art of the photoautotrophic components of known BLSSs is discussed. An initial comparison between plants and microalgae revealed the advantages of the latter with respect to edible biomass productivity, cultivation length, and growth requirements (i.e., water and illumination). Thus, the capability of microalgae as a biological component of BLSS was highlighted. Terrestrial and space experiments indicated the ability of different microalgal species to grow on human-derived wastewaters, space-derived nutrients (i.e., Martian regolith), and CO2, signifying their potential for in situ resource utilization. Nevertheless, the cultivation of microalgae under actual space conditions still faces many challenges that require further studies prior to inclusion into BLSS for long-duration space missions.

This work focused on demonstrating the use of dynamic time warping (DTW) as a metric for the elementary effects computation in Morris-based global sensitivity analysis (GSA) of model parameters in multivariate dynamical systems. One of the challenges of GSA on multivariate time-dependent dynamics is the modeling of parameter perturbation effects propagated to all model outputs while capturing time-dependent patterns. The study establishes and demonstrates the use of DTW as a metric of elementary effects across the time domain and the multivariate output domain, which are all aggregated together via the DTW cost function into a single metric value. Unlike the commonly studied coefficient-based functional approximation and covariance decomposition methods, this new DTW-based Morris GSA algorithm implements curve alignment via dynamic programing for cost computation in every parameter perturbation trajectory, which captures the essence of “elementary effect” in the original Morris formulation. This new algorithm eliminates approximations and assumptions about the model outputs while achieving the objective of capturing perturbations across time and the array of model outputs. The technique was demonstrated using an ordinary differential equation (ODE) system of mixed-order adsorption kinetics, Monod-type microbial kinetics, and the Lorenz attractor for chaotic solutions. DTW as a Morris-based GSA metric enables the modeling of parameter sensitivity effects on the entire array of model output variables evolving in the time domain, resulting in parameter rankings attributed to the entire model dynamics.

Wastewater (WW) analysis is a critical step in various operations, such as the control of a WW treatment facility, and speeding up the analysis of WW quality can significantly improve such operations. This work demonstrates the capability of neural network (NN) regression models to estimate WW characteristic properties such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia (NH3-N), total dissolved substances (TDS), total alkalinity (TA), and total hardness (TH) by training on WW spectral reflectance in the visible to near-infrared spectrum (400–2000 nm). The dataset contains samples of spectral reflectance intensity, which were the inputs, and the WW parameter levels (BOD, COD, NH3-N, TDS, TA, and TH), which were the outputs. Various NN model configurations were evaluated in terms of regression model fitness. The mean-absolute-error (MAE) was used as the metric for training and testing the NN models, and the coefficient of determination (R2) between the model predictions and true values was also computed to measure how well the NN models predict the true values. The highest R2 (0.994 for training set and 0.973 for testing set) and lowest MAE (0.573 mg/L BOD, 6.258 mg/L COD, 0.369 mg/L NH3-N, 6.98 mg/L TDS, 2.586 m/L TA, and 0.014 mmol/L TH) were achieved when NN models were configured for single-variable output compared to multiple-variables output. Hyperparameter grid-search and k-fold cross-validation improved the NN model prediction performance. With online spectral measurements, the trained neural network model can provide non-contact and real-time estimation of WW quality at minimum estimation error.

The prospect of humans inhabiting planetary bodies is gaining interest among research and development communities, with the moon being considered as a transitory base camp and Mars the next planet humans will inhabit. NASA's Mission to Mars program is set to have humans inhabiting Mars within on-planet space camps by the Year 2030, which has tremendously increased research and development for space exploration-including research oriented toward human life support in long-term planetary lodging camps. The sustenance of human life on Mars will not be trivial due to the unavailability of an appropriate atmosphere and usable water. This situation requires a self-sustaining human life support system that can provide the basic needs such are breathable air, potable water, food, and energy. The feasibility of sending a payload with resources adequate to support long-term human inhabitation is not reasonable, which means every resource within a Mars space camp is valuable, including human-produced wastes. A biorefinery system that treats wastewater and can also produce valuable products such as oxygen, food, and energy offers a form of circular utilization of valuable resources. To conduct research for such systems requires a wastewater influent that is representative of the wastewater to be generated by the space crew within this isolated, confined environment, which is different from what is generated on Earth due to limited variability in diet, human activity, and lifestyle in this confined area. Collection of actual wastewater influent from an isolated environment supporting humans is challenging. Additionally, to ensure a safe working environment in the laboratory and avoid the imposed threat of handling actual human feces, the proposed synthetic, non-human feces containing wastewater influent formulation offers an easy-to-produce and safer-to-handle option. This paper reviews several synthetic wastewater compositions that have been formulated for space exploration purposes. None of the formulations were found to be realistic nor adequate for a space-camp-type scenario. Thus, the formulation of a synthetic wastewater for simulating a wastewater influent from a human space-based camp is proposed in this paper. In addition, the physical, chemical, and biodegradation characteristics of the final formulation designed are presented to illustrate the value of the proposed influent formulation.

This work focused on demonstrating the capability of unsupervised machine learning techniques in detecting impending anomalies by extracting hidden trends in the datasets of fuel economy and emissions of light-duty vehicles (LDVs), which consist of cars and light-duty trucks. This case study used the vehicles’ fuel economy and emissions testing datasets for vehicle model years 2015 to 2023 with a total of 34,602 data samples on LDVs of major vehicle manufacturers. Three unsupervised techniques were used: principal components analysis (PCA), K-Means clustering, and self-organizing maps (SOM). Results show that there are clusters of data that exhibit trends not represented by the dataset as a whole. Fuel CO vs. Fuel Economy has a negative correlation in the whole dataset (r = −0.355 for LDVs model year 2022), but it has positive correlations in certain sample clusters (e.g., LDVs model year 2022: r = +0.62 in a K-Means cluster where the slope is around 0.347 g−CO/mi/MPG). A time series analysis of the results of clustering indicates that Test Procedure and Fuel Type, specifically Test Procedure 11 and Fuel Type 26 as defined by the US EPA, could be the contributors to the positive correlation of CO and Fuel Economy. This detected peculiar trend of CO-vs.-Fuel Economy is an impending anomaly, as the use of Fuel 26 in emissions testing with Test Procedure 11 of US-EPA has been increasing through the years. With the finding that the clustered data samples with positive CO-vs.-Fuel Economy correlation all came from vehicle manufacturers that independently conduct the standard testing procedures and not data from US-EPA testing centers, it was concluded that the chemistry of using Fuel 26 in performing Test Procedure 11 should be re-evaluated by US-EPA.

Wood adhesives play a vital role in the adhesive industry,butin the past, many wood composites were manufactured with significantamounts of toxic components and volatile organic compounds, most ofwhich are petroleum derivatives. The use of renewable, protein-richelements in adhesive production has been discussed significantly asa promising alternative compared to petroleum-based adhesives in thewood industry. Natural proteins are attracting attention as sustainablysourced and formaldehyde-free adhesives due to their minimal environmentalfootprint. The present review details the current assessment of formaldehyde-freeadhesive systems for particulate composites. Of the viable optionsfor protein feedstocks, agricultural proteins, animal byproduct proteins,algae proteins, and biosolid proteins are some of the many sustainablealternatives for the growing adhesive industry. Improved knowledgeof protein properties of a multitude of possible feedstocks for adhesiveproduction is needed to relate the adhesive performance before andafter wood bondage. The goal of this review is to present possibilitiesof applying multiple protein alternatives for adhesive productionand to determine the effectiveness in terms of strength, water resistivity,and need for cross-linking.

Microalgae are multi-purpose microbial agents due to their capability to efficiently sequester carbon dioxide and produce valuable biomass such as protein and single-cell oils. Formulation and tuning of microalgae kinetics models can significantly contribute to the successful design and operation of microalgae reactors. This work aimed to demonstrate the capability of self-organizing map (SOM) algorithm to elucidate the patterns of parameter rankings in microalgae models subject to stochastic variations of input forcing functions–bioprocess influent component concentration levels. These stochastic variations were implemented on a modeled chemostat with a deterministic microalgae kinetic model consists of ten time-dependent variables and eighteen model parameters. The methodology consists of two major stages: (1) global sensitivity analysis (GSA) on the importance of model parameters with stochastic sampling of bioreactor influent component concentrations, and (2) training of self-organizing maps on the datasets of model parameter rankings derived from the GSA indices. Results reveal that functional principal components analysis can project at least 99% of the time-dependent dynamic patterns of the model variables on B-splines basis functions. The component planes for hexagonal lattice SOMs reveal that the sensitivity rankings some parameters in the algae model tested can be stable over a wide range of variations in the levels of influent component concentrations. Therefore, SOM can be used to reveal the trends in multi-dimensional data arrays arising from the implementation of GSA of kinetic models under stochastic perturbation of input forcing functions.

Sensitivity analysis (SA) for the influence of model parametric constants has been integral in the use of mathematical kinetic models for design and operation of various anaerobic digestion applications. Using Anaerobic Digestion Model No. 1 (ADM1) as case study, this work aimed to broaden the approach for SA on the time-dependent model outputs of anaerobic digestion models by demonstrating the use of functional principal component analysis (fPCA) scores as input analysis variables into global SA (GSA) for the influence of stoichiometric parameters in ADM1. The methodology involved the following: Morris’ screening design as the GSA technique; ADM1 biomass yield and product yield coefficients as GSA parameters; and ADM1 outputs transformation via fPCA to generate principal component (PC) scores for GSA. Results indicate that 95-99% of the variations in the time-dependent outputs can be captured by the PCs after fPCA transformation, and that the first PC is sufficient to represent the model outputs. Ranked Morris sensitivity indices calculated from the first PC scores revealed the stoichiometric parameters that dominantly affect kinetic responses and those that are least sensitive. The ranking of stoichiometric sensitivities can be used for various purposes including driving mechanisms identification, and mathematical model modification.

Right-of-way (ROW) land areas are required for all publicly owned transportation roadways representing over 40 million acres within the US alone. These relatively unused land assets could support potential farming land for plants and algae that contain high levels of lipids that could be used in the energy industry as an alternative fuel source. This process would offer many benefits including more efficient use of public land, eliminating mowing maintenance, increasing the bioenergy use in the US, providing visually appealing viewscapes, and helping to naturally reduce localized carbon dioxide. This paper analyzed the feasibility and potential optimization strategies of using this concept in the South-Eastern United States by scaling and comparing many of the benefits and risks associated with the selected lipid sources (soybeans, flax, sunflowers, Tung trees, Chinese tallow tree, and microalgae). Based on this assessment, the most attractive option appears to be growing flax in the winter and sunflowers in the summer with Tung Trees grown year-round as an alternative option. This would maximize lipids output while preserving and enhancing right-of-way land areas.

AbstractThis work experimentally demonstrates that wastewater activated sludge microbial consortia can utilize methane resulting in lipid content enhancement. Activated sludge was cultivated using a synthetic wastewater as culture media. After initial purging with air, analytical grade methane gas was added in the culture headspace. The cultures were cultivated in batch‐mode for 120 hours at 25°C in 500‐mL bioreactors with 125‐mL working liquid volume. Results indicate that methane gas was utilized by activated sludge microbial consortia under a similar pattern of microbial respiration as the control cultivated only with air. The activated sludge in the methane‐purged bioreactors showed lipid enhancement (0.123 ± 0.037 mg lipid per mg biomass) and biomass growth (0.626 ± 0.163 mg biomass per mg glucose plus methane), which were higher than those in the control runs (0.009 ± 0.034 mg lipid per mg biomass and 0.225 ± 0.133 mg biomass per mg glucose).

This study focused on demonstrating the use of a self-organizing map (SOM) algorithm to elucidate patterns among variables in simulated syngas combustion. The work was implemented in two stages: (1) modelling and simulation of syngas combustion under various feed composition and reactor temperature implemented in AspenPlusTM chemical process simulation software, and (2) pattern recognition among variables using SOM algorithm implemented in MATLAB. The varied levels of feed syngas composition and reactor temperature was randomly sampled from uniform distributions using the Morris screening technique creating four thousand eight hundred simulation conditions implemented in the process simulation which consequently produced a multivariate dataset used in the SOM analysis. Results show that cylindrical SOM topology models the dataset at lower quantization error and topographic error as compared to the rectangular SOM topology indicating suitability of the former for variables pattern elucidation for the simulated combustion. Nonetheless, the variables pattern between component planes from rectangular SOM (9 × 28 grid) and those from cylindrical SOM (9 × 28 grid) are almost similar, indicating that either rectangular or cylindrical architectures may be used for variables pattern analysis. The component planes of process variables from trained SOM are a convenient visualization of the trends across all process variables.

Adsorption for water and wastewater treatment has been the subject of many research in the scientific community, focusing mainly on either equilibrium or kinetic studies. Adsorption kinetics are commonly modeled using pseudo-first and pseudo-second order rate laws. Analyses of published works in the past two decades indicated that the pseudo-second order is considered to be the superior model as it can represent many adsorption systems. However, critical assessment of modeling techniques and practices suggests that its superiority could be a consequence of currently acceptable modeling norms which tend to favor the pseudo-second order model. The partiality was due to several modeling pitfalls that are often neglected. In addition, commonly used model validation tools are often used haphazardly and redundantly. As such, they cannot sufficiently provide any kind of certainty on the validity of a model. To eliminate modeling biasness, a new validation method was proposed and was then employed to re-examine previously published adsorption kinetic data.

The rapid increase in the population is expected to result in the approaching of design capacity for many US wastewater treatment plants (WWTPs) over the next decade. WWTPs treat both municipal and industrial wastewater influents, resulting in the production of biosolids after digestion. Biogas, a potential recovered alternative energy source, is also produced as an output from successful anaerobic digestion. More than 7M of dry tons/year of biosolids produced in the US are most often disposed in either landfills or land-applied (~80%). These options are becoming more challenging to implement due to increases in transportation costs and tipping fees, decreases in the availability of landfill/landfarm space, and most importantly, increased regulations. This situation is strongly encouraging WWTPs to find alternatives for the disposal of biosolids. Developing alternative management/disposal options for biosolids are evolving. One of the most attractive alternative option from a sustainability perspective are biorefineries (converts waste to commercial products), which are a fast-growing option given the push toward circular urban source economies (little to no waste generation). Anaerobic digestion has been widely applied in WWTPs to reduce the volume of activated sludge due to its low energy requirements, effective handling of fluctuations due to organic loading rate, relative flexibility with temperature and pH changes, and since biogas is produced that can be transformed into energy. Various pretreatment methods for waste sludges prior to digestion that have been studied to reduce solids production and increase the energetic content of the biogas are presented and discussed. Solids handling and management, which comprises ~60% of the operational cost of a WWTP, is estimated to save more than $100 M annually by achieving at least 20% reduction in the annual production of biosolids within the US. This review incorporates an assessment of various pretreatment methods to optimize the anaerobic digestion of waste sludges with a focus on maximizing both biosolids reduction and biogas quality.

This work demonstrates a computational approach for the exploration of the importance of biochemical mechanism parameters in anaerobic digestion models subjected to concentration variations of digestion feedstock components. The methodology consists of an algorithm integrating global sensitivity analysis (GSA), functional principal component analysis (FPCA), and rank-clustering techniques. The GSA-FPCA integration removes the time-varying character of GSA (Morris') indices while the rank-clustering step provides a statistical approach to grouping the ranks of parameter sensitivities as affected by feedstock component variations. To substantiate generalizations of findings, two digestion models of differing complexity were used: Case 1 single-component feedstock digestion model with substrate inhibition, and Case 2 rigorous model Anaerobic Digestion Model No. 1 (ADM1). Results indicate that 95-99% of the variations in the time-dependent outputs can be captured by the principal components (PCs) after FPCA transformation, and that the first PC is sufficient to represent the model outputs. This capability of the FPCA to capture overall response curve patterns reduces the dimensionality of model response perturbation measures and makes the resulting data projections onto time-independent sensitivity indices intuitive of response curve perturbations. Ranked Morris sensitivity indices calculated from the first PC scores revealed stoichiometric parameters that dominantly affect kinetic responses as well as least sensitive parameters. The GSA-FPCA-Clustering approach elucidates digestion kinetic patterns only obvious under a systematic GSA methodology.