Process-Based Climate Model Development Harnessing Machine Learning: II. Model Calibration From Single Column to Global
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54 ENVIRONMENTAL SCIENCES↗
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Characterization of Plasma Protein Alterations in Pregnant and Postpartum Individuals Living With HIV to Support Physiologically-Based Pharmacokinetic Model Development
Alterations in plasma protein concentrations in pregnant and postpartum individuals can influence antiretroviral (ARV) pharmacokinetics. Physiologically-based pharmacokinetic (PBPK) models can serve to inform drug dosing decisions in understudied populations. However, development of such models requires quantitative physiological information (e.g., changes in plasma protein concentration) from the population of interest. Objective: To quantitatively describe the time-course of albumin and α1-acid glycoprotein (AAG) concentrations in pregnant and postpartum women living with HIV. Serum and plasma protein concentrations procured from the International Maternal Pediatric Adolescent AIDS Clinical Trial Protocol 1026s (P1026s) were analyzed using a generalized additive modeling approach. Separate non-parametric smoothing splines were fit to albumin and AAG concentrations as functions of gestational age or postpartum duration. The analysis included 871 and 757 serum albumin concentrations collected from 380 pregnant (~20 to 42 wks gestation) and 354 postpartum (0 to 46 wks postpartum) women, respectively. Thirty-six and 32 plasma AAG concentrations from 31 pregnant (~24 to 38 wks gestation) and 30 postpartum women (~2–13 wks postpartum), respectively, were available for analysis. Estimated mean albumin concentrations remained stable from 20 wks gestation to term (33.4 to 34.3 g/L); whereas, concentrations rapidly increased postpartum until stabilizing at ~42.3 g/L 15 wk after delivery. Estimated AAG concentrations slightly decreased from 24 wks gestation to term (53.6 and 44.9 mg/dL) while postpartum levels were elevated at two wks after delivery (126.1 mg/dL) and subsequently declined thereafter. Computational functions were developed to quantitatively communicate study results in a form that can be readily utilized for PBPK model development. By characterizing the trajectory of plasma protein concentrations in pregnant and postpartum women living with HIV, our analysis can increase confidence in PBPK model predictions for HIV antiretrovirals and better inform drug dosing decisions in this understudied population.
60 APPLIED LIFE SCIENCES↗
Experimental Setup and Learning-Based AI Model for Developing Accurate PV Inverter Models
The integration of power electronics-based interfaces presents challenges due to the absence of detailed models and the high computational complexity. Generic models used in system studies lack accuracy in capturing converter dynamics. This paper proposes a data-driven approach developed from experimental setup data. This approach enhances accuracy in photovoltaic inverter modeling. We used two types of PV inverters in the experiment. The recorded experimental data undergo processing through a machine learning model. Results from the model trained through machine learning is also presented.
artificial intelligence↗
Improvement of the $\mathrm{BISON U_3Si_2}$ modeling capabilities based on multiscale developments to modeling fission gas behavior
Uranium silicide (U 3 Si 2 ) is a concept explored as a potential alternative to UO 2 fuel used in light water reactors (LWRs) since it may improve accident tolerance and economics due to its higher thermal conductivity and increased uranium density. U 3 Si 2 has been previously used in research reactors in the form of dispersion fuel, but operated at lower temperatures than commercial LWRs. The research reactor data illustrated that significant gaseous swelling occurs as the fuel burnup increases. Therefore, it is imperative to understand the fission gas behavior of U 3 Si 2 under higher temperature LWR operating conditions. In this work, molecular dynamics and phase-field modeling techniques are used to reduce the uncertainty in select modeling assumptions made in developing the fission gas behavior model for U 3 Si 2 in the BISON fuel performance code. These lower length scale informed models are then utilized in the validation of BISON U 3 Si 2 modeling capabilities to simulate the ATF-1 experiments irradiated in the Advanced Test Reactor (ATR). Sensitivity analysis (SA) and uncertainty quantification (UQ) are included as part of the validation process to identify where further experiments and lower length scale modeling would be beneficial. Here, the multiscale modeling approach utilized in this work can be applied to new fuel concepts being explored for both LWRs and advanced reactors (e.g., uranium nitride, uranium carbide).
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Summary of physics-based pulverization model development for high-burnup fuel
Summary to be presented at EPRI-DOE workshop on high burnup and accident tolerant fuel
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques
Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.
13 HYDRO ENERGY↗
Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning
This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. Additionally, the diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.
97 MATHEMATICS AND COMPUTING↗
Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology
The data of a nuclear utility or plant are stored in various isolated forms in different systems. These data have different structures and tools and are, therefore, used independently. The integration of data sources is often performed manually and on an as-needed basis to achieve a targeted cost saving. A holistic approach to the integration of the data would enable creating a data warehouse for a single nuclear power plant (NPP) or multiple plants. A data warehouse results in direct and indirect cost saving because it automates the manual search for data, enables sharing and comparison of data from various tools of a single or multiple plants, enable digital transformation of data, increase frequencies of needed data that are sparse in nature (such as failures signatures), reduces the need for training on various tools for plant staff, enables a holistic staff perception of plant activities, reveals cost-saving opportunities, and improves the visual perception of all parts a plant necessary for operations and maintenance. One of the main challenges to deploy a data warehouse is associated with the integration of data sources into a consistent data model using a standard ontology that is developed by and for the nuclear power industry. This model is non-existent and is a necessity to enable the nuclear data warehouse role. A data model contains objects that make up a given domain along with their attributes and relationships. It provides domain standardization and maintainability, setting forth the objects, attributes, and relationships within that domain and acting as the guide by which the various applications within that domain will structure their data and interchange. An ontology is defined as “a set of concepts and categories in a subject area or domain that shows their properties and the relations between them”. Ontologies consist of a taxonomy or directed rooted tree. A survey of the models and ontologies available to support a data integration aggregated model and ontology for nuclear deployment (DIAMOND) was performed and different standards that address specific aspects of the needed model and ontology were leveraged and integrated in this effort. In specific, the Basic Formal Ontology (BFO) and Lifecycle Modeling Language (LML) were leveraged for the base ontology. The ISO 4224 and 15926 were leveraged in the development of the overlapping nuclear and fossil data aspects. The electric power research institute (EPRI) power distribution common information model (CIM) was also reviewed and the experience gained was leveraged in the power generation scope of DIAMOND. The initial aim targeted by this effort was to develop the ontology definitions and model structure. This report documents the resulting model and ontology to develop DIAMOND. The report describes the various elements of the model development based on a growing tree development approach, i.e. DIAMOND’s tree structure is established with the core data aspects and is populated with a preliminary level of details. Fifteen sources of data in a nuclear power plant were considered and incorporated into DIAMOND at this stage of development, representing the majority of operations data sources in nuclear power plants. Additional data sources, scope, and a higher level of fidelity will be incorporated into DIAMOND next in collaboration with the nuclear industry and relevant stakeholders.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions
As climate models become increasingly complex, there is a growing need to comprehensively and systematically assess model performance with respect to observations. Given the increasing number and diversity of climate model simulations in use, the community has moved beyond simple model intercomparison and toward developing methods capable of benchmarking a large number of simulations against a suite of climate metrics. Here, we present a detailed review of evaluation and benchmarking methods and approaches developed in the last decade, focusing primarily on scientific implications for Coupled Model Intercomparison Project (CMIP) simulations and CMIP6 results that contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Based on this review, we explain the resulting contemporary philosophy of model benchmarking, and provide clear distinctions and definitions of the terms model verification, process validation, evaluation, and benchmarking. While significant progress has been made in model development based on systematic evaluation and benchmarking efforts, some climate system biases still remain. The development of open‐source community software packages has played a fundamental role in identifying areas of significant model improvement and bias reduction. We review the key features of several software packages that have been commonly used over the past decade to evaluate and benchmark global and regional climate models. Additionally, we discuss best practices for the selection of evaluation and benchmarking metrics and for interpreting the obtained results, the importance of selecting suitable sources of reference data and accurate uncertainty quantification.
Environmental sciences↗
Achievements and Ongoing Challenges for advanced CFD Boiling Closure Model Development using Physical Insights from Validation-Oriented High-Fidelity Boiling Experiment
The fundamental understanding of the boiling-associated heat transfer, including sliding bubble effect, is still challenging despite the intensive research for decades in both experimental and computational boiling research communities. One of the main difficulties of boiling experiment come from the fact that direct observation of the underlying principle through experiment is very difficult due to the high complexity of the boiling phenomenon. Also, since the heat transfer characteristics associated with boiling is the result of non-linear interaction of various physical parameters coupled to each other, it is challenging to find the relationship between the parameters for modeling. This paper discusses the authors’ past and ongoing boiling research under CASL (Consortium for Advanced Simulation of Light Water Reactors) program, including the efforts to overcome the difficulties of boiling measurement and model development. In particular, we focus on describing the major achievements, lessons learnt, and ongoing challenges for the CFD boiling closure model development based on the high-fidelity subcooled flow boiling experiment at Texas A&M University (TAMU). The major research achievements discussed through this paper include (i) novel boiling measurement strategy and high-fidelity validation data production, (ii) dedicated effort to identify the critical measurement issues in boiling experiment, (iii) new physical insight into sliding bubble heat transfer mechanisms, and (iv) new boiling closure model development. Discussion is also made on the ongoing challenges for the boiling closure model development and the future research plan based on the lessons learnt.
42 ENGINEERING↗
Photovoltaic Manufacturing Consortium (PVMC) – Enabling America’s Solar Revolution
The U.S. Photovoltaic Manufacturing Consortium (US-PVMC) is an industry-led consortium which was created with the mission to accelerate the research, development, manufacturing, field testing, commercialization, and deployment of next-generation solar photovoltaic technologies. Formed as part of the U.S. Department of Energy's (DOE) SunShot initiative, and headquartered in New York State, PVMC is managed by the State University of New York Polytechnic Institute (SUNY Poly) at the Colleges of Nanoscale Science and Engineering. PVMC is a hybrid of industry-led consortium and manufacturing development facility, with capabilities for collaborative and proprietary industry engagement. Through its technology development programs, advanced manufacturing development facilities, system demonstrations, and reliability and testing capabilities, PVMC has demonstrated itself to be a recognized proving ground for innovative solar technologies and system designs. PVMC comprises multiple locations, with the core manufacturing and deployment support activities conducted at the Solar Energy Development Center (SEDC), and the core Si wafering and metrology technologies being headed out of the University of Central Florida. The SEDC provides a pilot line for proof-of-concept prototyping, offering critical opportunities to demonstrate emerging concepts in PV manufacturing, such as evaluations of innovative materials, system components, and PV system designs. The facility, located in Halfmoon NY, encompasses 40,000 square feet of dedicated PV development space. The infrastructure and capabilities housed at PVMC includes PV system level testing at the Prototype Demonstration Facility (PDF), manufacturing scale cell & module fabrication at the Manufacturing Development Facility (MDF), cell and module testing, reliability equipment on its PV pilot line, all integrated with a PV performance database and analytical characterizations for PVMC and its partners test and commercial arrays. Additional development and deployment support are also housed at the SEDC, such as cost modeling and cost model based development activities for PV and thin film modules, components, and system level designs for reduced LCOE through lower installation hardware costs, labor reductions, soft costs and reduced operations and maintenance costs. The progression of the consortium activities started with infrastructure and capabilities build out focused on CIGS thin film photovoltaics, with a particular focus on flexible cell and module production. As marketplace changes and partners objectives shifted, the consortium shifted heavily towards deployment and market pull activities including Balance of System, cost modeling, and installation cost reduction efforts along with impacts to performance and DER operational costs. The consortium consisted of a wide array of PV supply chain companies from equipment and component suppliers through national developers and installers with a particular focus on commercial scale deployments (typically 25 to 2MW installations). With DOE funding ending after the fifth budget period, the advantages and disadvantages of such a consortium is detailed along with potential avenues for self-sustainability is reviewed.
14 SOLAR ENERGY↗
A Composition-Based Approach for Predicting Hanford Tank Waste Liquid Viscosity
Liquid viscosity is an important Hanford waste physical property that governs, in part, the performance of key Hanford flowsheet unit operations and processes, including waste filtration, ion exchange, pumping and transport, and mixing. Evaluation and optimization of these processes rely on liquid viscosity correlations subject to substantial error (at least ±60%) that can lead to overly conservative, and sometimes physically unachievable, limits for validation and operation of these unit operations. The current liquid viscosity correlations used for the Hanford flowsheet attempt to capture the effects of specific waste chemistry (namely, the speciation of various salts) on liquid viscosity using liquid density alone. It is postulated that the ±60% or greater error associated with these “density-based” liquid viscosity predictions is driven in part by the inability of waste density to capture the full impact of waste chemistry on liquid viscosity, and that improved prediction accuracy could be achieved by implementing a composition-based approach to evaluating liquid viscosity for Hanford wastes. Recent publications, namely those of Lencka et al. (1998), Hu (2004), and Laliberté (2007a), provide correlations and comprehensive, consolidated databases of single-salt solution viscosity that enable composition-based predictions for Hanford-type liquids (i.e., concentrated multi-component aqueous electrolyte solutions). In the current study, the viscosities of Hanford wastes and waste simulants were predicted using a composition-based model developed by Laliberté (2007a) (hereafter referred to as the “Laliberté viscosity model”) to evaluate its accuracy relative to the current density-based models used to predict waste viscosity for engineering applications at the Hanford Site. A viscosity database that captured the composition and viscosity of wastes and waste simulants was developed to support this evaluation. To facilitate application of the Laliberté viscosity model, waste and waste simulant compositions were converted to an equivalent salt recipe composed of salts with known viscosity coefficients using a number of simplifying assumptions that have the potential to impact the fidelity of the model prediction. Despite these simplifications, the Laliberté viscosity model was able to predict viscosity more accurately than the density-based correlations currently available for Hanford flowsheet use, such as that proposed in RPP-RPT- 51652. The use of composition-based viscosity models, like that proposed by Laliberté (2007a), shows promise for leading to improved prediction of liquid waste viscosity. As implemented in this study, application of the Laliberté viscosity model could lead to a decrease in the uncertainty associated with liquid viscosity prediction by as much as a factor of two, allowing for reduction in unit operation design conservatism and for the more realistic selection of test simulants for flowsheet process evaluation and optimization, all of which could potentially reduce flowsheet design margins, lower costs and reduce duration of the Hanford mission, and reduce Hanford flowsheet risks.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Gas-Induced Motion of a Piston in a Vibrated Liquid-Filled Housing
We present that models and experiments are developed to investigate how a small amount of gas can cause large rectified motion of a piston in a vibrated liquid-filled housing when piston drag depends on piston position so that damping is nonlinear even for viscous flow. Two bellows serve as surrogates for the upper and lower gas regions maintained by Bjerknes forces. Without the bellows, piston motion is highly damped. With the bellows, the piston, the liquid, and the two bellows move together so that almost no liquid is forced through the gaps between the piston and the housing. This Couette mode has low damping and a strong resonance: the piston and the liquid vibrate against the spring formed by the two bellows (like the pneumatic spring formed by the gas regions). Near this resonance, the piston motion becomes large, and the nonlinear damping produces a large rectified force that pushes the piston downward against its spring suspension. A recently developed model based on quasi-steady Stokes flow is applied to this system. A drift model is developed from the full model and used to determine the equilibrium piston position as a function of vibration amplitude and frequency. Corresponding experiments are performed for two different systems. In the two-spring system, the piston is suspended against gravity between upper and lower springs. Lastly, in the spring-stop system, the piston is pushed up against a stop by a lower spring. Model and experimental results agree closely for both systems and for different bellows properties.
42 ENGINEERING↗
Integrating Advanced Modeling and Accelerated Testing for a Modernized Fuel Qualification Paradigm
With the increasing interest in sodium fast reactor technology, as seen by applications to the U.S. Nuclear Regulatory Commission for the OKLO Aurora plant, fuel testing for the TerraPower Traveling Wave Reactor, and the impending construction and startup of the versatile test reactor (VTR), a modernized, accelerated approach to fuel qualification is needed. To guide this effort, a Phenomena Identification Ranking Table–styled analysis was performed for a U-Pu-Zr sodium-free annular fuel system. This analysis evaluated a series of fuel design properties and parameters against their contributions to key fuel performance phenomena. The resulting priority parameters were then reviewed against existing modeling and experimental capabilities to support investigation of the highest-priority parameters. A pathway for qualification was then established using highthroughput, high-volume experiments from MiniFuel and FAST in parallel with advanced physics-based model development. This effort outlines how the first stages of qualification can be reduced from the typical 20+-year development cycle to 5 to 7 years by deploying accelerated irradiation testing platforms. As with any accelerated test, these methods are prototypic in some aspects and less so in others; however, by coupling with advanced fuel performance modeling and simulation capabilities, the larger space of irradiation parameters and material response provided offers advantages for the validation of physics-based models supporting the deployment of novel fuel designs. As a test case, this paper utilizes a proposed Mark II fuel system for the upcoming VTR. Thus, an accelerated qualification method can be tested for the development of MARK II driver fuel so that by the time of VTR startup, lead test assemblies for a Mark II fuel can be initiated.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Experimental Setup and Learning-Based AI Model for Developing Accurate PV Inverter Models [Slides]
The integration of power electronics-based interfaces presents challenges due to the absence of detailed models and the high computational complexity. Generic models used in system studies lack accuracy in capturing converter dynamics. This paper proposes a data-driven approach developed from experimental setup data. This approach enhances accuracy in photovoltaic inverter modeling. We used two types of PV inverters in the experiment. The recorded experimental data undergo processing through a machine learning model. Results from the model trained through machine learning is also presented.
14 SOLAR ENERGY↗
A mechanistic model for creep and thermal aging in Alloy 709
This report describes a physics-based model for creep and thermal aging in Alloy 709. Alloy 709 is an advanced austenitic alloy, targeted for use in future Sodium Fast Reactors (SFRs) and other advanced reactors. The material has superior high temperature properties compared to currently qualified 316 and 304 stainless steels. However, the available creep and thermal aging test database for Alloy 709 is significantly more limited compared to the historical materials. The physics-based model developed here is one way to accelerate the qualification of the material by providing more accurate long-term predictions for creep properties and thermal aging, compared to current empirical time-extrapolate techniques. The crystal plasticity finite element model is used to predict the deformation and failure of alloy 709. The same setup for the CPFE model is used in both the baseline model calibration process and the simulation campaigns for parameter inference. Specific constitutive choices are made for Alloy 709 to capture the primary deformation mechanisms. The dislocation creep formulation developed by Hu and Cocks is extended to account for coupled precipitation formation and the grain boundary cavitation model developed by Sham, Needleman, et al. is used to model grain boundary cavitation-induced failure. A novel update algorithm is proposed to render the semi-discrete constitutive update for the Sham-Needleman model unconditionally stable. A progressive calibration approach is adopted based on the observations that several types of material responses can be effectively decoupled. A surrogate model is trained based on full-fledged CPFE simulations to accelerate the forward model evaluations, and stochastic variational inference (SVI) is used to calibrate the unknown microstructural model parameters. The calibrated mechanistic model is used to predict the long-term creep life of Alloy 709, and the predictions are compared against classical empirical approaches.
36 MATERIALS SCIENCE↗
CDL2PLC translator v0.1.0
The CDL-PLC translator aims at translating control sequences for building energy systems from the CDL CXF format to the PLCopen XML format. The CDL CXF developed at LBL within the OpenBuildingControl project, and now being standardized via ASHRAE Standard 231P, enables expressing control sequences developed in the simulation environment Modelica in a JSON format. The PLCopen XML is an existing exchange format standardized in IEC 61131-10 for Programmable Logic Controllers (PLCs) following the IEC 61131 standard as one target system of CDL among others. The translation from the CDL CXF to the PLCopen XML contributes to a seamless workflow from the model-based development of control sequences in simulation environments, which is not building practice today, and their digital implementation on building controllers, which replaces graphical and textual documents used for this purpose today. The translator is at a prototypical stage and enables, as a proof of concept, the translation of very simple control sequences composed of 4 selected function blocks out of 137 function blocks defined in CDL. The translation includes the connection of inputs and outputs of function blocks and the expression of a control function in CDL to the equivalent code in IEC 61131-3.
Walther, Karl↗
Inorganic characterization of switchgrass biomass using laser-induced breakdown spectroscopy
The inorganic characterization of 74 samples of switchgrass using laser-induced breakdown spectroscopy (LIBS) was undertaken. Determination of ash and inorganic elements content in biomass materials is vital for feedstock screening for bioconversion processes. Hierarchical models using principal component analysis (PCA) and partial least square analysis (PLS) were used to determine the presence of specific elemental micronutrients that are important in determining plant health for robust biomass production. LIBS uses a 532 nm laser with 45 mJ of laser power to excite the samples of switchgrass plant material and the emission of all the elements present in the plant samples were recorded in single spectra with a wide wavelength range of 200–800 nm. The results were compared to the laboratory standard technique, e.g., ICP-OES technique, to determine the true values for major micronutrients such as, silicon (Si), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), and sulfur (S). Overall, our objectives were: 1) To determine the spectral features of switchgrass containing different amounts of these elements and 2) To examine the viability of this technique for determining the quality of the feedstock in terms of its inorganic composition. Cross-validation results showed that the broad-based model developed is promising for inorganics prediction in switchgrass. The LIBS validation prediction for the micronutrient elements mentioned here have been obtained. The regression coefficients for Si, were obtained to be 0.995, 0.994 for calibration and validation respectively, in case of Ca the regression coefficients were, 0.994 and 0.992 for calibration and validation. Similarly, in the case of Mg and K these were calculated to be 0.992 and 0.985, and 0.994 and 0.993 respectively. The regression coefficients are not as good as those for the elements mentioned, in case of the two elements S and P. They are 0.957, and 0.878, and 0.952 and 0.894 respectively for calibration, validation for the two elements. This demonstrates that LIBS-based techniques are inherently well suited for diverse environmental applications. Furthermore, LIBS along with PLS model can show capability in determining the viability of switchgrass as a biomass in the production of biofuels and survivability of switchgrass in processes associated with climate change. LIBS can help determining which switchgrass would be appropriate for a specific conversion process that favors low ash content overall or low value of specific inorganics.
59 BASIC BIOLOGICAL SCIENCES↗