Results for “policy evaluation” — Page 12

DOE OSTI2020

RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning

Today’s high-performance computing (HPC) platforms are still dominated by batch jobs. Accordingly, effective batch job scheduling is crucial to obtain high system efficiency. Existing HPC batch job schedulers typically leverage heuristic priority functions to prioritize and schedule jobs. But, once configured and deployed by the experts, such priority functions can hardly adapt to the changes of job loads, optimization goals, or system settings, potentially leading to degraded system efficiency when changes occur. To address this fundamental issue, we present RLScheduler, an automated HPC batch job scheduler built on reinforcement learning. RLScheduler relies on minimal manual interventions or expert knowledge, but can learn high-quality scheduling policies via its own continuous ‘trial and error’. We introduce a new kernel-based neural network structure and trajectory filtering mechanism in RLScheduler to improve and stabilize the learning process. Through extensive evaluations, we confirm that RLScheduler can learn high-quality scheduling policies towards various workloads and various optimization goals with relatively low computation cost. Moreover, we show that the learned models perform stably even when applied to unseen workloads, making them practical for production use.

Zhang, Di↗

NASA NTRS1992

An effective write policy for software coherence schemes

The authors study the write behavior and evaluate the performance of various write strategies and buffering techniques for a MIN-based multiprocessor system using the simple software coherence scheme. Hit ratios, memory latencies, total execution time, and total write traffic are used as the performance indices. The write-through write-allocate no-fetch cache using a write-back write buffer is shown to have a better performance than both write-through and write-back caches. This type of write buffer is effective in reducing the volume as well as bursts of write traffic. On average, the use of a write-back cache reduces by 60 percent the total write traffic generated by a write-through cache.

Chen, Yung-Chin↗

DOE OSTI2020

Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response

Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1°C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2°C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3°C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23°C(±0.12°C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Learning Stochastic Parametric Differentiable Predictive Control Policies

We present a scalable unsupervised learning-based method for obtaining explicit control policies for model predictive control problems for stochastic linear systems with additive uncertainties subject to nonlinear chance constraints. We call the proposed method stochastic parametric differentiable predictive control (SP-DPC), which extends the recently proposed deterministic DPC policy optimization algorithm. We formulate the SP-DPC as a deterministic approximation to the stochastic parametric constrained optimal control problem via independent sampling of the problem's parameters and uncertainties. This formulation allows us to directly compute the policy gradients via automatic differentiation of the problem's value function, evaluated over sampled parameters and uncertainties. In particular, the computed expectation of the problem's value function is backpropagated through the finite-time closed-loop system rollouts parametrized by a known nominal system dynamics model and neural control policy. We also provide theoretical probabilistic guarantees on closed-loop stability and chance constraints satisfaction for systems controlled by learned neural policies. We demonstrate the computational efficiency and scalability of the proposed policy optimization algorithm in three numerical examples, including systems with a large number of states or subject to nonlinear constraints.

Drgona, Jan↗

NASA NTRS2005

Evolving Multi Rover Systems in Dynamic and Noisy Environments

In this chapter, we address how to evolve control strategies for a collective: a set of entities that collectively strives to maximize a global evaluation function that rates the performance of the full system. Addressing such problems by directly applying a global evolutionary algorithm to a population of collectives is unworkable because the search space is prohibitively large. Instead, we focus on evolving control policies for each member of the collective, where each member is trying to maximize the fitness of its own population. The main difficulty with this approach is creating fitness evaluation functions for the members of the collective that induce the collective to achieve high performance with respect to the system level goal. To overcome this difficulty, we derive member evaluation functions that are both aligned with the global evaluation function (ensuring that members trying to achieve high fitness results in a collective with high fitness) and sensitive to the fitness of each member (a member's fitness depends more on its own actions than on actions of other members). In a difficult rover coordination problem in dynamic and noisy environments, we show how to construct evaluation functions that lead to good collective behavior. The control policy evolved using aligned and member-sensitive evaluations outperforms global evaluation methods by up to a factor of four. in addition we show that the collective continues to perform well in the presence of high noise levels and when the environment is highly dynamic. More notably, in the presence of a larger number of rovers or rovers with noisy sensors, the improvements due to the proposed method become significantly more pronounced.

Tumer, Kagan↗

DOE OSTI2022

Improving energy performance metrics to maximize the benefits of disruptive technologies

Advances in appliance and equipment systems' technologies also cause changes in their technical characteristics that can affect energy consumption and associated greenhouse gas emissions. Energy performance evaluation methods that use testing and metrics can evolve to account for energy consumption contributed from new technological characteristics. However, there is no systematic effort to enable the advancement of test procedures and performance metrics to maximize the energy, climate, and financial benefits of disruptive technologies. Therefore, it is important for countries to regularly update their energy-efficiency policy programs, such as standards and labels, by improving test procedures and metrics to reflect the innovation in emerging technologies as well as to mitigate the risk of deploying obsolete technology. This paper shows how energy performance metrics have been improving in selected appliances and equipment and provides insights to design a systematic effort to improve metrics and test procedures at the speed of ongoing changes in technologies and markets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2006

The New Jettison Policy for the International Space Station

During more than seven years of operations by the International Space Station (ISS), approximately three dozen pieces of debris were released and subsequently cataloged by the U.S. Space Surveillance Network (SSN). The individual mass of these objects ranged from less than 1 kg to 70 kg. Although some of these debris were separated from the ISS accidentally, some were intentionally cast-off, especially the larger items. In addition, small operational satellites are candidates for launch from the ISS, such as the TNS-O satellite deployed from ISS in March 2005. Recently an official ISS Jettison Policy was developed to ensure that decisions to deliberately release objects in the future were based upon a complete evaluation of the benefits and risks to the ISS, other resident space objects, and people on the Earth. The policy identifies four categories of items which might be considered for release: (1) items that pose a safety issue for return on-board a visiting vehicle, (2) items that negatively impact ISS utilization, return, or on-orbit stowage manifests, (3) items that represent an EVA timeline savings, and (4) items that are designed for jettison. Some of the principal issues to be addressed during this evaluation process are the potential for the object to recontact the ISS within the first two days after jettison, the potential of the object to breakup prior to reentry, the ability of the SSN to track the object, and the risk to people on Earth from components which might survive reentry. This paper summarizes the history of objects released from ISS, examines the specifics of the ISS jettison policy, and addresses the overall impact of ISS debris on the space environment.

Johnson, Nicholas L.↗

DOE OSTI2021

Making Data-Driven Policy Decisions for the Nation’s First Building Energy Performance Standards

Nearly every major U.S. city has committed itself to ambitious climate action goals – for Washington, DC this means a 50 percent reduction in greenhouse gases by 2032 and carbon neutrality by 2050. In support of these goals, Washington, DC has passed one of the most aggressive and practical climate action bills in the nation—with the Clean Energy DC Omnibus Act, DC became the first city in the U.S. to adopt energy performance standards for existing buildings. DC’s Building Energy Performance Standards (BEPS) require energy efficiency improvements for all commercial and multifamily buildings that do not meet a sector-specific minimum ENERGY STAR score or equivalent metric, with iterative compliance cycles every five years that will accelerate the pace of whole building retrofits. This paper explores this revolutionary policy framework and uses two data analysis projects that DC conducted to evaluate the potential impact of the BEPS and move towards carbon neutrality. First, we analyze the potential energy savings and greenhouse gas reductions, as well as potential cost impacts, from the implementation of a BEPS policy in DC We then examine the role of BEPS in a carbon neutrality strategy, how BEPS savings iterate over time, and what additional existing building improvements will be driven by the gravitational pull of new building codes on median performance. The paper highlights the benefits and limitations of such data-driven approaches to support policy decisions. Finally, we will review ongoing BEPS implementation, including expected policy directions, critical supportive programs, and lessons learned to date.

Bergfeld, Katie↗

DOE OSTI2020

Systems Theory Principles and Complex Systems Engineering Concepts for Protection and Resilience in Critical Infrastructure: Lessons from the Nuclear Sector

Part of the Presidential Policy Directive 21 (PPD-21) (PPD 2013) mandate includes evaluating safety, security, and safeguards (or nonproliferation) mechanisms traditionally implemented within the nuclear reactors, materials, and waste sector of critical infrastructure—including a complex, dynamic set of risks and threats within an all-hazards approach. In response, research out of Sandia National Laboratories (Sandia) explores the ability of systems theory principles (hierarchy and emergence) and complex systems engineering concepts (multidomain interdependence) to better understand and address these risks and threats. Herein, this Sandia research explores the safety, safeguards, and security risks of three different nuclear sector-related activities—spent nuclear fuel transportation, small modular reactors, and portable nuclear power reactors—to investigate the complex and dynamic risk related to the PPD-21-mandated all-hazards approach. This research showed that a systems-theoretic approach can better identify inter-dependencies, conflicts, gaps, and leverage points across traditional safety, security, and safeguards hazard mitigation strategies in the nuclear reactors, materials, and waste sector. Resulting from this, mitigation strategies from applying systems theoretic principles and complex systems engineering concepts can be (1) designed to better capture interdependencies, (2) implemented to better align with real-world operational uncertainties, and (3) evaluated as a systems-level whole to better identify, characterize, and manage PPD-21's all hazards strategies.

42 ENGINEERING↗

DOE OSTI2024

Can Wholesale Electricity Markets Achieve Resource Adequacy and High Clean Energy Generation Targets in the Presence of Self-Interested Actors?

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

capacity expansion↗

DOE OSTI2020

Analysis of Space-Conditioning Loads in Commercial Buildings

Space conditioning end-uses, which include heating, cooling, and ventilation, represent a significant fraction of commercial building energy use, with a wide variety of heating and cooling technology options available in the market. In the interest of improving the overall efficiency of heating, ventilation and air-conditioning (HVAC) technologies, governments, utilities and private sector entities have implemented a variety of market transformation policies that aim to influence consumer purchase decisions. To evaluate the costs and benefits of such programs, analysts typically postulate a hypothetical default equipment choice, and compare it to one that provides comparable service with lower energy and/or power use. The corresponding reduced operating cost provides a benefit that offsets the potential higher cost of improved efficiency. Typically, life-cycle cost or cash-flow analyses are used to quantify the net economic benefit. These analyses require the capability to assess how a given equipment design would perform across a broad range of characteristics, both of the building and of the local weather. While these assessments can be performed using customized building simulations, it is generally not practical to develop and validate detailed building simulation code to cover all the potential variations of equipment design and installation. An alternative, and somewhat simpler, approach is to solely use detailed building simulations to generate time series of heating and cooling loads in commercial buildings. These loads can then be used as input to more detailed, stand-alone engineering models that simulate HVAC system performance under different equipment designs. This approach was used to evaluate a range of high-efficiency commercial packaged air conditioner design options for the Department of Energy’s Appliance and Equipment Standards Program (DOE-EERE 2015). While there may be some loss of precision relative to full simulation, the accuracy of this approach is sufficient for practical applications of cost-benefit analysis. This report describes the development of a database of commercial building heating and cooling loads, generated using the EnergyPlus software package, a whole building energy use model supported by the Department of Energy (DOE-EERE 2020a). EnergyPlus takes as input a set of configuration files that describe the building itself (size, zoning, envelope characteristics, etc.) and the various systems within it (HVAC, lighting, water heating, etc.). This analysis uses a publicly available collection of commercial reference buildings (CRB), comprised of sixteen building types and three vintages (DOE-EERE 2020b; Deru et al 2011). Each building is simulated in eighteen different locations, covering a wide range of climatic conditions. The prototype building description files assign the type of HVAC equipment used, and capacities across climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Wholesale Electricity Markets and Resource Adequacy with High Clean Energy Generation Targets

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

capacity expansion↗

DOE OSTI2024

Wholesale Electricity Market Design to Support Resource Adequacy

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

agent based modeling↗

NASA NTRS1978

Lifetime Cost and Performance model for photovoltaic power systems

This paper describes the approach and procedures of the Lifetime Cost and Performance (LCP) model for photovoltaic power systems. The LCP model is designed to evaluate the impact of alternative initial design and recurrent policy decisions on both cost and power output over the lifetime of a photovoltaic power plant. LCP is, therefore, useful to system designers and operators for addressing questions relating to optimal system configuration, installation activities, level of effort and timing of operations/maintenance actions, allowable degradation and replacement options.

Borden, C. S.↗

DOE OSTI2021

BETO 2021 Peer Review - Enzyme Engineering and Optimization (EEO)

The FY2020 SOT economic model for the DMR BDO process indicates that the production and use of biomass degrading enzymes represents ~10% of the MFSP of BDO. We are thus working to enhance the performance of the dominant cellulase enzyme, Cel7A. In Fy2018-19, we selected 100 promising genes from the "Cel7A wheel of life" and cloned them into T. reesei using a constitutive promotor. This natural diversity screening resulted in the discovery of several enzymes exhibiting improved characteristics relative to the industry standard Cel7A from T. reesei (Tr). Growth of the transformed host on glucose insured that the Cre1 induced cellulase expression cascade was suppressed, thus enabling purification of the target gene product. The first top performing enzyme found was from P. funiculosum (Pf). Other top performing enzymes were identified from T. aculeatus, T. terrestris, and A. oryzae. Computational modeling predicted the structural subsites responsible for the improvements, which were cloned into the sequence of PfCel7A. The recombinant enzymes were expressed in T. reesei, purified, and tested. Testing was not possible with SOT relevant substrates, so the best-case substrate was used; solids from the DMR process subjected to a novel dilute alkaline wash to remove the "lignin shield." Two chimeric Cel7A enzymes show improved performance (1.2 to 1.35x) relative to PfCel7A, which in turn showed considerable improvement (1.6x) relative to T. reesei Cel7A. These new enzymes have been provided to our industrial partner for evaluation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Assessing Customer Experience and Business Models around Price-to-Device Communication and Smart Control Pathways in CalFlexHub

California is facing three major challenges in electrical grid operation: renewable overgeneration, steep evening ramping, and growing peak demand. The state has identified dynamic retail price response as a key strategy evidenced by CPUC’s Dynamic Rates proceeding and CEC’s Load Management Standards. Furthermore, the CEC launched a $16M “California Load Flexibility Research and Deployment Hub (CalFlexHub)” administered by Berkeley Lab to accelerate price-response flexible load technologies in buildings and EV charging. There are more than 16 laboratory and field demonstration projects in CalFlexHub, each demonstrating innovative automated price-response technologies. CalFlexHub tests various pathways through which hourly price signals and triggered control commands are communicated to load-flexible devices such as smart thermostats, heat pumps, water heaters, and EVs. We identified seven unique communication and control pathways, which involve combinations of third-party cloud, device OEM’s cloud, building central gateway, and local controller in between the price server and the load-flexible devices. It is important for utilities and policy makers to understand the long-term implications of each pathway in designing future programs and creating related policies and mandates for market transformation. We propose an evaluation framework including the following aspects: ● Functionality: connectivity and uptime, resilience, and optimization; ● Customer experience: simplicity in setup, troubleshooting support, continuity, customer choice, first cost, and ongoing cost; ● Business model and scalability: advance interoperability, holistic solution, bridge unique gap, customer base, and value streams and pricing structures. In this paper, we identify emerging business models associated with each communication pathway and discuss their positive features and challenges from the above aspects.

Liu, Jingjing↗

DOE OSTI2026

Techno-Economic, Feasibility, and Life Cycle Analysis of Renewable Propane: 2025 Update

To clarify the current and future landscape for renewable propane (RP) production, this work evaluates the value proposition of recovering RP from existing and planned hydroprocessed esters and fatty acids (HEFA) biorefineries and surveys emerging technologies under development or deployment. HEFA biorefineries co-produce a propane-rich fuel gas stream, normally used to meet HEFA process heat requirements, from which propane can be recovered and sold to create an additional revenue stream alongside liquid transportation fuels such as renewable diesel (RD) and sustainable aviation fuel (SAF). This report updates and extends a 2022 analysis of RP recovery from HEFA facilities by escalating capital and operating costs to 2024 prices, incorporating recent policy developments (including the Section 45Z Clean Fuel Production Credit), evaluating RP recovery for both RD- and SAF-focused HEFA facilities at two scales (3,000 and 75,000 barrels per day of feedstock), and quantifying the impact of RP recovery on HEFA liquid-fuel carbon intensity (CI) and associated tax credits using the 45ZCF-GREET model. For a 3,000 BPD RD-focused HEFA facility, approximately 3.5 million gallons per year (MGPY) of RP can be recovered; in this base case, the estimated payback period is 18 months based on the total installed cost of the RP recovery equipment and 36 months based on the total capital investment for the entire RP recovery project. The payback period is slightly shorter for the analogous SAF-focused configuration (approximately 4.3 MGPY RP). Sensitivity analysis shows that CAPEX magnitude, RP recovery plant scale, and CI-driven tax credit valuations are the dominant determinants of project viability. RP recovery may increase the CI of HEFA liquid fuels, which can reduce liquid-fuel tax credits (a key revenue stream for the HEFA biorefinery) and lengthen payback periods. However, RP recovery generally remains economically favorable across a wide range of plausible scenarios and market conditions. The report also summarizes emerging pathways that could expand future RP supply.

09 BIOMASS FUELS↗

DOE OSTI2025

Technology Case Study: Economic, Sustainability, and Deployment Considerations for Sustainable Aviation Fuels Produced via Lignocellulosic Sugar Catalysis

This report presents a technology case study reflecting one exemplary representative pathway for the conversion of lignocellulosic sugars to sustainable aviation fuels (SAF) via aqueous phase reforming (APR) catalysis, considered within a broader integrated biorefinery framework based on biochemical processing operations. While far from the only option for converting sugars to SAF, this pathway was selected as a case study here based on its relatively high technology maturity and simplistic processing approach (avoiding complex separations or other equipment scalability challenges), coupled with the potential for high fuel yields and favorable costs/carbon intensities with opportunities for further near-term optimization. The report considers key process integration and engineering design considerations for a modeled hypothetical, nth-plant commercial biorefinery, reflecting a number of processing options and parameters envisioned to be achievable as future goals. Resultant outputs from Aspen Plus process simulations are evaluated through techno-economic and life cycle analyses (TEA and LCA), including implications for marginal cost of CO2 abatement and inclusion of currently-applicable policy incentives. Moving beyond base case configurations, a number of alternative scenarios are also evaluated for their ability to further improve economics, greenhouse gas (GHG) emissions, and marginal cost of abatement, highlighting a path to achieve deep decarbonization goals of more than 70% GHG reduction for SAF (with the potential to reach net-negative carbon intensities in some cases) under reasonable fuel production costs. The report also highlights future opportunities and gaps for further research on this technology pathway.

09 BIOMASS FUELS↗