SOLICITATION NOTICE
A -- Evaluation of the Behavior and Control of Selenium (Se) in Coal-fired Boilers
- Notice Date
- 7/14/2010
- Notice Type
- Presolicitation
- Contracting Office
- Environmental Protection Agency, U.S. Epa, Cincinnati Procurement Operations Division, 4411 Montgomery Road - Suite 300, Cincinnati, OH 45212
- ZIP Code
- 45212
- Solicitation Number
- RFQ-OH-10-00129
- Response Due
- 7/29/2010
- Archive Date
- 8/29/2010
- Point of Contact
- Point of Contact, Kathleen Rechenberg, Purchasing Agent, Phone (513) 487-2853
- E-Mail Address
-
U.S. Environmental Protection Agency
(Rechenberg.Kathleen@epamail.epa.gov)
- Small Business Set-Aside
- N/A
- Description
- NAICS Code: 541620 This is a combined synopsis/solicitation for commercial items prepared in accordance with the format in FAR Subpart 12.6, as supplemented with additional information included in this notice. This announcement constitutes the only solicitation; quotations are beingrequested and a written solicitation will not be issued. Refer to RFQ-OH-10-00129 when responding to this request. The solicitation document and incorporated provisions and clauses are those in effect through Federal Acquisition Circular 05-44. This acquisition is set-a-side for small business. The NAICS code is 541620-Environmental Consulting Services, the size standard is seven (7) million dollars. The U.S. Environmental Protection Agency (EPA), Office of Research and Development, National Risk Management Research Laboratory, Air Pollution Prevention and Control Division, located in RTP, NC has a requirement for an Evaluation of the Behavior and Control of Selenium (Se) in Coal-fired Boilers. All services shall be provided for the U.S. EPA National Risk Management Research Laboratory, Air Pollution Prevention and Control Division, located in RTP, North Carolina. The F.O.B. point shall be destination. Price quotes shall include any setup charges. The provision at FAR 52.212-1, Instructions to Offerors-Commercial, applies to this to this acquisition. The provision at FAR 52.212-2, Evaluation-Commercial Items is applicable and the specific evaluation criteria are included in the Technical Evaluation Criteria. Offers shall provide descriptive technical literature in sufficient detail to demonstrate that the technical approach meet the minimum requirements specified in Statement of Work. Statement of Work: Statement of Work: Evaluation of the Behavior and Control of Selenium (Se) in Coal-fired Boilers Background EPA'S Office of Air Quality Procedures and Standards (OAQPS - Office of Air & Radiation), is preparing to develop a Maximum Achievable Control Technology (MACT) - based rule to control hazardous air pollutant (HAP) emissions from coal- and oil-fired electric utility boilers. This "Utility MACT" may include the use of "surrogate" measurements - i.e., inferred control of a HAP (or HAPs) based on the measurement or control of another pollutant or other component. To support this effort, EPA's Office of Research & Development (ORD) is conducting tests, using a range of coal-types and process configurations, in the Multipollutant Control Research Facility (MPCRF). The MPCRF is a pilot-scale combustion research facility located in the High Bay Building (HBB) at EPA's Research Triangle Park, NC campus. The combustor is rated at 4 MMBtu/hr (approximately 1.2 MWt). It is capable of firing all types of pulverized coal, natural gas and No. 2 fuel oil. The combustor is equipped with low NOX burners and a SCR unit (with Cormetech catalyst) for NOX control. The system has an electrostatic precipitator (ESP) and a fabric filter (FF) - positioned in parallel - for Particular Matter (PM) control. The flue gas can be directed to either the ESP or the FF. The facility also uses a wet lime-based flue gas desulfurization (FGD) scrubber for control of SO2 emissions. The system is equipped with continuous emissions monitors (CEMs) for on-line measurement of O2, CO2, NOX (NO and NO2), SO2, CO and Hg. There are multiple sampling ports through out the gas flow path. The facility is designed for ease of modification so that various control technologies and configurations can be tested. The facility has a series of heat exchangers to remove heat such that the flow path of the flue gas has a similar temperature profile to that seen in a full-scale power plant. One of the goals of the MPCRF tests (referred to as the "Surrogacy Tests") is to collect data to judge the viability on the use of surrogates for many of the HAPs. The control of the non-mercury metal HAPs (e.g., arsenic, selenium, cadmium, lead, etc.) will be compared against the control of the bulk particulate matter (PM) in the primary PM control device (either the ESP or the FF). Helble (2000) previously surveyed emissions of trace elements from many power plants compiled by EPRI in the PISCES field-sampling program. With the exception of As, Se, and Hg, the emission rates of other trace elements could be modeled adequately using the rate of emissions of particulate matter from the ESP, because these elements are mostly found in the collected fly ash (i.e., they are controlled consistently along with the bulk PM). Mercury can be controlled using a variety of options (or combinations of technologies), such as activated carbon injection (ACI), halogen injection, wet scrubbing, etc. Selenium, in particular, seems to vary considerably and be much less predictable than the other non-Hg metal HAPs. The behavior of selenium in coal-fired power plants is relevant to air emissions of this hazardous air pollutant, as well as to emissions of selenium in wastewater discharges (ash ponds, scrubber water treatment). There is a need for a fundamentally based, predictive model for vapor-solid partitioning of selenium in coal-fired boilers and in downstream air pollution control equipment, in order to predict selenium concentrations in fly ash and vapor-phase selenium entering FGD scrubbers. Objective The objective of the proposed work is to advance the knowledge of selenium behavior in coal-fired boilers and to assess the potential for controlling emissions of selenium. In this project, pilot-scale data will be used to improve an existing model for selenium. The model will be used to carry out scoping studies to advance understand the effects of changing conditions and coal composition on selenium partitioning in coal-fired boilers and the potential for using sorbents to control selenium air emissions. Statement of Work / Tasks Task 1. Literature Review. The objective is to provide background on the relevant chemistry of selenium, under the conditions of a coal-fired boiler. A better understanding of the speciation of selenium in the gas and solid phases in combustion systems will help to improve the model to predict selenium partitioning in coal-fired boilers. The contractor shall provide a survey of the available literature on (a) gas-phase behavior of selenium in high temperature systems, (b) chemical reactions between selenium and common constituents in flue gas and ash, including unburned carbon and activated carbon, (c) full-scale data on selenium partitioning in coal-fired boilers, and (d) leaching of selenium from coal fly ash. Task 2. Modeling of Pilot-Scale Data. The objective of this task is to use pilot-scale data generated in EPA's 4 MMBtu/hr (1.2 MWt) pulverized coal furnace, located in Research Triangle Park, as input for existing model(s) for metals partitioning in coal-fired boilers. As part of surrogate evaluations for Hazardous Air Pollutants (HAPs), EPA ORD (with EPA OAR and EPA OAQPS) is conducting pilot-scale tests focused on HAPs, including selenium. Data from these tests will be provided to the contractor. The contractor will analyze the data and use the data as input to existing models of metals behavior in the pilot-scale combustor to create a Se-specific model (or, perhaps, to enhance an existing Se-specific model). The results from this effort will lead to improvements in the predictive capability of the selenium model. Task 3. Model Scoping Studies. The objective of this task is to use the model to better understand the effects of changing conditions and coal composition on selenium partitioning in coal-fired boilers in which a variety of coal types and temperature profiles can be found. Selenium partitioning (from coal to ESP outlet) will be predicted for coals of different compositions (ranks) and different air pollution control devices. In addition, calculations will be carried out to simulate the injection of calcium- and iron-containing sorbents at various locations in the boiler. The results of this task will be useful for better planning of measurement campaigns and evaluation of sorbents for removal of selenium. Quality Assurance The contractor shall prepare a quality assurance project plan for research model development and application projects in accordance with Attachment #1. It shall be submitted to the EPA project manager and the EPA QA staff for review and approval. Work involving the collection or use of environmental data shall not commence until the QAPP has received official approval from the EPA QA staff. Deliverables and Schedule The quality assurance project plan shall be delivered to EPA within 4 weeks of the award of this contract. The contractor shall prepare brief monthly reports documenting the progress during the project. The contractor shall provide a final report, documenting the results of Tasks 1 through 3, plus recommendations for future work, shall be prepared. The final report shall be the final deliverable on this project. The final report shall be provided to the EPA Project Officer within 9 months after award. The Final Report Shall Contain:"A thorough literature review and background on the Chemistry of Selenium under high temperature combustion conditions. "A description of the Model for Tasks 2 and 3 from the aforementioned Statement of Work."A description of the pilot-scale date used for the modeling work. "A description and discussion of the modeling results. The contractor shall comply with all requirements as delineated on the "Quality Assurance Planning Requirements Form" included with the intramural action. RESEARCH MODEL DEVELOPMENT AND APPLICATION PROJECTS NRMRL Quality Assurance (QA) Requirements In accordance with EPA Order 5360.1 A2, conformance to ANSI/ASQC E4-1994 (http://www.epa.gov/quality/exmural.html ) must be demonstrated by submitting the quality documentation specified herein. All quality documentation shall be submitted to the Government for review. The Government will review and return the quality documentation, with comments, and indicate approval or disapproval. If the quality documentation is not approved, it must be revised to address all comments and shall be resubmitted to the Government for approval. Work involving environmental data collection, generation, use, or reporting shall not commence until the Government has approved the quality documentation. The quality documentation shall be submitted to the Government at least thirty (30) days prior to the beginning of any environmental data gathering or generation activity in order to allow sufficient time for review and revisions to be completed. After the Government has approved the quality documentation, the Contractor shall also implement it as written and approved by the Government. Any EPA-funded project/program may be subject to a QA audit. TO BE SUBMITTED PRE-AWARD (mark all that apply): ? NRMRL's Quality System Specifications: (1) A description of the organization's Quality System (QS) and information regarding how this QS is documented, communicated and implemented; (2) An organizational chart showing the position of the QA function; (3) Delineation of the authority and responsibilities of the QA function; (4) The background and experience of the QA personnel who will be assigned to the project; and (5) The organization's general approach for accomplishing the QA specifications in the SOW. ? Quality Management Plan: prepared in accordance with R-2 - EPA Requirements for Quality Management Plans (EPA/240/B-01/002) March, 2001, http://www.epa.gov/quality/qs-docs/r2-final.pdf TO BE SUBMITTED POST-AWARD (mark all that apply): ? NRMRL's Quality System Specifications: (1) A description of the organization's Quality System (QS) and information regarding how this QS is documented, communicated and implemented; (2) An organizational chart showing the position of the QA function; 07/14/08 A-2 (3) Delineation of the authority and responsibilities of the QA function; (4) The background and experience of the QA personnel who will be assigned to the project; and (5) The organization's general approach for accomplishing the QA specifications in the SOW. ? Quality Management Plan: prepared in accordance with R-2 - EPA Requirements for Quality Management Plans (EPA/240/B-01/002) March, 2001, http://www.epa.gov/quality/qs-docs/r2-final.pdf ? Category I or II Quality Assurance Project Plan (QAPP): prepared in accordance with R-5 - EPA Requirements for QA Project Plans (EPA/240/B-01/003) March, 2001 http://www.epa.gov/quality/qs-docs/r5-final.pdf X Category III or IV QAPP: prepared in accordance with applicable sections of the following NRMRL QAPP Requirements List(s) which is(are) included in this attachment: ? QAPP Requirements for Measurement Projects ? QAPP Requirements for Secondary Data Projects X QAPP Requirements for Research Model Development and/or Application Projects ? QAPP Requirements for Software Development Projects ? QAPP Requirements for Method Development Projects ? QAPP Requirements for Design, Construction, and/or Operation of Environmental Technology Projects ADDITIONAL QA RESOURCES: EPA's Quality System Website: http://www.epa.gov/quality/ EPA's Requirements and Guidance Documents: http://www.epa.gov/quality/qa_docs.html ------------------------------------------------------------------------------------------------------------------------------ NRMRL QAPP REQUIREMENTS FORRESEARCH MODEL DEVELOPMENT AND APPLICATION PROJECTS GENERAL REQUIREMENTS: Include cover page, distribution list, approvals, and page numbers. COVER PAGE Include the Division/Branch, project title, revision number, EPA technical lead, QA category, organization responsible for QAPP preparation, and date. 1. PROJECT DESCRIPTION AND OBJECTIVES (MODEL DEVELOPMENT AND MODEL APPLICATION) In this document, "project" can mean (a) development or substantial modification of a model for application to address a general problem; (b) application of an existing model (including minor modification to the existing model) to address a specific problem; or (c) a development or substantial modification and application of a model to address a specific problem. 1.1 State the purpose of the project and list the project objective(s). Indicate whether a new model will be developed or an existing model will be used.1.2 Describe the problem, the data to be generated by the model, how the data will be used to address the problem, and the intended users of the data. Describe the environmental system/setting to be modeled, where the model will be applied, and the circumstances and scenarios to be considered for the modeled system. 2. ORGANIZATION AND RESPONSIBILITIES (MODEL DEVELOPMENT AND MODEL APPLICATION) 2.1 Identify all project personnel, including QA, and related responsibilities for each participating organization, as well as their relationship to other project participants.2.2 Include a project schedule that includes key milestones. 3. MODEL SELECTION (MODEL APPLICATION ONLY) 3.1 Discuss model selection with respect to how it will be used and how it is consistent with the project objectives. Include fundamental details such as whether the model will be used to predict the world beyond the model or in scenario analysis of the model itself. Describe the limits to where the model is applicable.3.2 Provide a description of the model attributes/capabilities required for the project. This description should include hardware requirements and restrictions. Provide an overview of the candidate model attributes, including:o Model origin and its original purpose, if applicableo Model structure (e.g., stochastic vs. deterministic, structural framework)o Parameters and variableso The algorithms and equations that have been developed to support the model theory, along with the sources of the algorithmso Spatial extent (individual, group, population)o Spatial resolution (location independent/dependent, dimensionality)o Temporal extent (length of modeling period)o Temporal resolution (time step)3.3 Identify the model to be used or, if the model has not yet been selected, describe the process to be used for the selection of an existing model.3.4 Identify specific requirements for application of the selected model for this specific purpose (e.g., current and appropriate data, parameter values, assumptions). 4. MODEL DESIGN (MODEL DEVELOPMENT ONLY) 4.1 Describe the conceptual model(s) for the system, including model parameters.4.2 Identify algorithms and equations that have been developed to support the model theory, or if such equations are not already available, describe the process used to develop these equations.4.3 Specify required sources for model databases and any requirements for these data (e.g., quality, quantity, spatial, and temporal applicability). If data sources are not currently known, describe the criteria used to identify sources. Describe how any data gaps will be filled. 5. MODEL CODING (MODEL DEVELOPMENT ONLY) 5.1 Discuss the requirements for model code development, where applicable.5.2 Identify computer hardware and software requirements.5.3 Discuss requirements for code verification. 6. MODEL CALIBRATION (MODEL DEVELOPMENT AND MODEL APPLICATION) Calibration is the process of adjusting model parameters within physically defensible ranges until the resulting predictions give the best possible or desired degree of fit to the observed data. Calibration should be applied each time the model is modified. 6.1 Discuss how the model will be calibrated.6.2 Identify the type and source of data (e.g., new data, existing data, professional judgment, expert opinion elicitation) that will be used to calibrate the model, including any requirements for the data (quality, quantity, and spatial and temporal applicability). If data sources are not currently known, describe the criteria
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