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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Model-Based Systems Engineering for Capturing Mission Architecture System Processes with an Application Case Study - Orion Flight Test 1

Model-based Systems Engineering (MBSE) is an emerging methodology that can be leveraged to enhance many system development processes. MBSE allows for the centralization of an architecture description that would otherwise be stored in various locations and formats, thus simplifying communication among the project stakeholders, inducing commonality in representation, and expediting report generation. This paper outlines the MBSE approach taken to capture the processes of two different, but related, architectures by employing the Systems Modeling Language (SysML) as a standard for architecture description and the modeling tool MagicDraw. The overarching goal of this study was to demonstrate the effectiveness of MBSE as a means of capturing and designing a mission systems architecture. The first portion of the project focused on capturing the necessary system engineering activities that occur when designing, developing, and deploying a mission systems architecture for a space mission. The second part applies activities from the first to an application problem - the system engineering of the Orion Flight Test 1 (OFT-1) End-to-End Information System (EEIS). By modeling the activities required to create a space mission architecture and then implementing those activities in an application problem, the utility of MBSE as an approach to systems engineering can be demonstrated.

Orion Flight Test 1 (OFT-1)↗
NASA NTRS2002

ISO 9000 and/or Systems Engineering Capability Maturity Model?

For businesses and organizations to remain competitive today they must have processes and systems in place that will allow them to first identify customer needs and then develop products/processes that will meet or exceed the customers needs and expectations. Customer needs, once identified, are normally stated as requirements. Designers can then develop products/processes that will meet these requirements. Several functions, such as quality management and systems engineering management are used to assist product development teams in the development process. Both functions exist in all organizations and both have a similar objective, which is to ensure that developed processes will meet customer requirements. Are efforts in these organizations being duplicated? Are both functions needed by organizations? What are the similarities and differences between the functions listed above? ISO 9000 is an international standard of goods and services. It sets broad requirements for the assurance of quality and for management's involvement. It requires organizations to document the processes and to follow these documented processes. ISO 9000 gives customers assurance that the suppliers have control of the process for product development. Systems engineering can broadly be defined as a discipline that seeks to ensure that all requirements for a system are satisfied throughout the life of the system by preserving their interrelationship. The key activities of systems engineering include requirements analysis, functional analysis/allocation, design synthesis and verification, and system analysis and control. The systems engineering process, when followed properly, will lead to higher quality products, lower cost products, and shorter development cycles. The System Engineering Capability Maturity Model (SE-CMM) will allow companies to measure their system engineering capability and continuously improve those capabilities. ISO 9000 and SE-CMM seem to have a similar objective, which is to document the organization's processes and certify to potential customers the capability of a supplier to control the processes that determine the quality of the product or services being produced. The remaining sections of this report examine the differences and similarities between ISO 9000 and SE-CMM and make recommendations for implementation.

Sampson E. Gholston↗
NASA NTRS2016

A Structured, Model-Based Systems Engineering Methodology for Operations System Design

Two widely accepted techniques for lowering the cost and risk of developing systems are (1) the use of a defined systems engineering (SE) process or methodology and (2) the reuse of existing (previously built) system components. The first technique is represented, for example, in materials published by NASA (e.g., NASA Systems Engineering Handbook) or by professional societies such as INCOSE (International Council on Systems Engineering). Well-formed SE techniques provide value by establishing the proper scope of the system (e.g., requirements), and by identifying and resolving problems relatively early in project lifecycles, when fixes are less expensive. The second technique (reuse) is applied most commonly to hardware and software; it seeks to avoid replicating design and implementation costs while also reducing risk by placing proven capabilities into operational use. In this paper, we outline a methodology combining these two techniques and extending reuse beyond hardware and software to foundational aspects of a Mission Operation System’s (MOS) design. We describe the system design artifacts that result (e.g., requirements, design documentation), as well as the reusable patterns and elements of the design, and their interrelationships. This approach is enabled by model-based systems engineering (MBSE) techniques and tools and is currently available in SysML form as a plug-in to MagicDraw. Additionally, usage of a rigorous MBSE approach allows for training materials and tutorials to be packaged within the overall model itself. The results of such an approach include decreased cost and risk during the design phase, improved ability of the MOS development team to investigate trade spaces and identify impacts to important flight-ground trade studies. Such results extend into decreased costs and risk in later phases due to improved design, decreased need for late fixes or development of "glue-ware" or scripts to fill unanticipated gaps in functionality, and improved ability to identify and plan testing and other validation activities. Finally, lower operational costs can be expected, both due to improved quality of the MOS, increased ease of maintaining updated knowledge of system configuration, and the fact that training and procedural materials are also updated at the same time as accepted system changes.

Bindschadler, Duane L.↗
NASA NTRS2011

NASA System Safety Handbook: System Safety Framework and Concepts for Implementation - Volume 1

System safety assessment is defined in NPR 8715.3C, NASA General Safety Program Requirements as a disciplined, systematic approach to the analysis of risks resulting from hazards that can affect humans, the environment, and mission assets. Achievement of the highest practicable degree of system safety is one of NASA's highest priorities. Traditionally, system safety assessment at NASA and elsewhere has focused on the application of a set of safety analysis tools to identify safety risks and formulate effective controls.1 Familiar tools used for this purpose include various forms of hazard analyses, failure modes and effects analyses, and probabilistic safety assessment (commonly also referred to as probabilistic risk assessment (PRA)). In the past, it has been assumed that to show that a system is safe, it is sufficient to provide assurance that the process for identifying the hazards has been as comprehensive as possible and that each identified hazard has one or more associated controls. The NASA Aerospace Safety Advisory Panel (ASAP) has made several statements in its annual reports supporting a more holistic approach. In 2006, it recommended that "... a comprehensive risk assessment, communication and acceptance process be implemented to ensure that overall launch risk is considered in an integrated and consistent manner." In 2009, it advocated for "... a process for using a risk-informed design approach to produce a design that is optimally and sufficiently safe." As a rationale for the latter advocacy, it stated that "... the ASAP applauds switching to a performance-based approach because it emphasizes early risk identification to guide designs, thus enabling creative design approaches that might be more efficient, safer, or both." For purposes of this preface, it is worth mentioning three areas where the handbook emphasizes a more holistic type of thinking. First, the handbook takes the position that it is important to not just focus on risk on an individual basis but to consider measures of aggregate safety risk and to ensure wherever possible that there be quantitative measures for evaluating how effective the controls are in reducing these aggregate risks. The term aggregate risk, when used in this handbook, refers to the accumulation of risks from individual scenarios that lead to a shortfall in safety performance at a high level: e.g., an excessively high probability of loss of crew, loss of mission, planetary contamination, etc. Without aggregated quantitative measures such as these, it is not reasonable to expect that safety has been optimized with respect to other technical and programmatic objectives. At the same time, it is fully recognized that not all sources of risk are amenable to precise quantitative analysis and that the use of qualitative approaches and bounding estimates may be appropriate for those risk sources. Second, the handbook stresses the necessity of developing confidence that the controls derived for the purpose of achieving system safety not only handle risks that have been identified and properly characterized but also provide a general, more holistic means for protecting against unidentified or uncharacterized risks. For example, while it is not possible to be assured that all credible causes of risk have been identified, there are defenses that can provide protection against broad categories of risks and thereby increase the chances that individual causes are contained. Third, the handbook strives at all times to treat uncertainties as an integral aspect of risk and as a part of making decisions. The term "uncertainty" here does not refer to an actuarial type of data analysis, but rather to a characterization of our state of knowledge regarding results from logical and physical models that approximate reality. Uncertainty analysis finds how the output parameters of the models are related to plausible variations in the input parameters and in the modeling assumptions. The evaluation of unrtainties represents a method of probabilistic thinking wherein the analyst and decision makers recognize possible outcomes other than the outcome perceived to be "most likely." Without this type of analysis, it is not possible to determine the worth of an analysis product as a basis for making decisions related to safety and mission success. In line with these considerations the handbook does not take a hazard-analysis-centric approach to system safety. Hazard analysis remains a useful tool to facilitate brainstorming but does not substitute for a more holistic approach geared to a comprehensive identification and understanding of individual risk issues and their contributions to aggregate safety risks. The handbook strives to emphasize the importance of identifying the most critical scenarios that contribute to the risk of not meeting the agreed-upon safety objectives and requirements using all appropriate tools (including but not limited to hazard analysis). Thereafter, emphasis shifts to identifying the risk drivers that cause these scenarios to be critical and ensuring that there are controls directed toward preventing or mitigating the risk drivers. To address these and other areas, the handbook advocates a proactive, analytic-deliberative, risk-informed approach to system safety, enabling the integration of system safety activities with systems engineering and risk management processes. It emphasizes how one can systematically provide the necessary evidence to substantiate the claim that a system is safe to within an acceptable risk tolerance, and that safety has been achieved in a cost-effective manner. The methodology discussed in this handbook is part of a systems engineering process and is intended to be integral to the system safety practices being conducted by the NASA safety and mission assurance and systems engineering organizations. The handbook posits that to conclude that a system is adequately safe, it is necessary to consider a set of safety claims that derive from the safety objectives of the organization. The safety claims are developed from a hierarchy of safety objectives and are therefore hierarchical themselves. Assurance that all the claims are true within acceptable risk tolerance limits implies that all of the safety objectives have been satisfied, and therefore that the system is safe. The acceptable risk tolerance limits are provided by the authority who must make the decision whether or not to proceed to the next step in the life cycle. These tolerances are therefore referred to as the decision maker's risk tolerances. In general, the safety claims address two fundamental facets of safety: 1) whether required safety thresholds or goals have been achieved, and 2) whether the safety risk is as low as possible within reasonable impacts on cost, schedule, and performance. The latter facet includes consideration of controls that are collective in nature (i.e., apply generically to broad categories of risks) and thereby provide protection against unidentified or uncharacterized risks.

Dezfuli, Homayoon↗
DOE OSTI2021

Perceived Conflicts of Systems Engineering in Early-Stage Research and Development 1

Early Systems Research and Development (ESR&D) is one of the most crucial phases in the product development process. It both blends and blurs the lines between science and engineering, and requires a risk-based, disciplined, and graded approach to effectively manage scope, cost, and complexity of the final product. Many leaders, program managers, and scientists are unwilling to involve systems engineering because of the perception that systems engineering is heavily process oriented, adds unnecessary costs, and should be applied only to mature technologies. The value of systems engineering as applied to ESR&D is unclear to these key individuals. The unfortunate result is that system engineering is not applied to ESR&D. This results in R&D efforts that may have solved the wrong problem, selected the wrong architecture, require technical rework, have difficulty transitioning later maturity levels, and result in higher R&D costs and extended development timelines. This work discusses the difficulty of introducing systems engineering to the research and early development process and their inclination perspectives of researchers, engineers, and managers. The article shall offer potential means to manage the cultural transformation of early adoption of right-sized systems engineering in ESR&D and reverse the attitudinal positions.

42 ENGINEERING↗
NASA NTRS2023

Human Systems Integration: Managing Risk in Anesthesia

The practice of anesthesia relies on clinicians’ ability to safely manage increasingly complex equipment. Devices such as ventilators, drug infusion pumps, and physiologic monitors use sophisticated algorithms to deliver care, but most clinicians are only trained to manage automated systems during normal operation. Few if any receive training on how to manage system failures. Although manufacturers are required to consult with human factors engineers as part of the equipment design process, most pieces of equipment are ultimately brought to market without extensive input from clinicians. Systems in the operating room can be as simple as an oxygen tank, or as complex as a multi-institutional healthcare organization. Humans are also a complex system, and play a critical role in the domains of operations, design, fabrication, maintenance, repair, and ultimately, dismantling and closeout. HSI seeks to provide a means for advocating the human side of the system. Human Systems Integration (HSI) is the cross disciplinary process used as part of the Systems Engineering process to reduce risk in systems. HSI professionals consider the human, hardware, and software elements of system design to optimize system performance and improve safety. HSI professionals work in domains of study that include training, management, human factors engineering, safety, and occupational health, among others. This article discusses the role of systems in the practice of anesthesia, and how consideration of the human during all phases of the system life cycle helps manage risks and promote a better patient outcome.

Human Factors↗
NASA NTRS2014

A Model-Based Approach to Developing Your Mission Operations System

Model-Based System Engineering (MBSE) is an increasingly popular methodology for designing complex engineering systems. As the use of MBSE has grown, it has begun to be applied to systems that are less hardware-based and more people- and process-based. We describe our approach to incorporating MBSE as a way to streamline development, and how to build a model consisting of core resources, such as requirements and interfaces, that can be adapted and used by new and upcoming projects. By comparing traditional Mission Operations System (MOS) system engineering with an MOS designed via a model, we will demonstrate the benefits to be obtained by incorporating MBSE in system engineering design processes.

mos↗
NASA NTRS1989

A systems engineering management approach to resource management applications

The author presents a program management response to the following question: How can the traditional practice of systems engineering management, including requirements specification, be adapted, enhanced, or modified to build future planning and scheduling systems for effective operations? The systems engineering management process, as traditionally practiced, is examined. Extensible resource management systems are discussed. It is concluded that extensible systems are a partial solution to problems presented by requirements that are incomplete, partially immeasurable, and often dynamic. There are positive indications that resource management systems have been characterized and modeled sufficiently to allow their implementation as extensible systems.

Hornstein, Rhoda Shaller↗
NASA NTRS2005

NESTA: NASA Engineering Shuttle Telemetry Agent

The Spaceport Processing Systems Branch at NASA Kennedy Space Center has developed and deployed an agent based tool to monitor the Space Shuttle's ground processing telemetry stream. The application, the NASA Engineering Shuttle Telemetry Agent, increases situational awareness for system and hardware engineers during ground processing of the Shuttle's subsystems. The agent provides autonomous monitoring of the telemetry stream and automatically alerts system engineers when predefined criteria have been met. Efficiency and safety are improved through increased automation. Sandia National Labs' Java Expert System Shell is employed as the rule engine. The shell's predicate logic lends itself well to capturing the heuristics and specifying the engineering rules of this spaceport domain. The declarative paradigm of the rule-based agent yields a highly modular and scalable design spanning multiple subsystems of the Shuttle. Several hundred monitoring rules have been written thus far with corresponding notifications sent to Shuttle engineers. This paper discusses the rule-based telemetry agent used for Space Shuttle ground processing and explains the problem domain, development of the agent software, benefits of AT technology, and deployment and sustaining engineering of the product.

Semmel, Glenn S.↗
NASA NTRS1972

Integration of safety engineering into a cost optimized development program.

A six-segment management model is presented, each segment of which represents a major area in a new product development program. The first segment of the model covers integration of specialist engineers into 'systems requirement definition' or the system engineering documentation process. The second covers preparation of five basic types of 'development program plans.' The third segment covers integration of system requirements, scheduling, and funding of specialist engineering activities into 'work breakdown structures,' 'cost accounts,' and 'work packages.' The fourth covers 'requirement communication' by line organizations. The fifth covers 'performance measurement' based on work package data. The sixth covers 'baseline requirements achievement tracking.'

Ball, L. W.↗
NASA NTRS2006

Systems Integration Processes for NASA's Crew Launch Vehicle

NASA's Exploration Initiative will require development of many new elements to constitute a robust system of systems. New launch vehicles are needed to place cargo and crew in stable low earth orbit. This paper examines the systems integration processes NASA is utilizing to ensure integration and control of propulsion and non-propulsion elements within NASA's Crew Launch Vehicle (CLV). The objective of the CLV is to provide the transportation capabilities to meet the Constellation Program requirements for delivering a Crew Exploration Vehicle (CEV) or other payload to Low Earth Orbit (LEO) in support of the lunar and Mars missions. The CLV must successfully provide the capability within cost and schedule with an acceptable risk approach. This paper will describe in detail the systems engineering management processes that will be applied to assure CLV Project success through complete and efficient technical integration. Discussion of specific processes for requirements development and verification, integrated design and analysis, integrated simulation and testing and the integration of reliability, maintainability and supportability (RMS) into the design will also be included. The CLV Project is broken logically into elements by the major hardware groupings, and associated management, system engineering, and integration functions. The processes to be described herein are designed to integrate within these CLV elements and among the other Constellation projects. Launch vehicle stack integration (CLV to CEV, and Ground and Flight Operations integration) throughout the life cycle, including integrated vehicle performance through orbital insertion, recovery of the first stage, and reentry of the upper stage will also be discussed. The processes for decomposing requirements to the Elements and ensuring that requirements have been correctly validated, decomposed, allocated, and that the verification requirements are properly defined to ensure that the system design meets requirements will be discussed.

Reuter, James L.↗
NASA NTRS2006

Systems Integration Processes for NASA Ares I Crew Launch Vehicle

NASA's Exploration Initiative will require development of many new elements to constitute a robust system of systems. New launch vehicles are needed to place cargo and crew in stable Low Earth Orbit (LEO). This paper examines the systems integration processes NASA is utilizing to ensure integration and control of propulsion and nonpropulsion elements within NASA's Crew Launch Vehicle (CLV), now known as the Ares I. The objective of the Ares I is to provide the transportation capabilities to meet the Constellation Program requirements for delivering a Crew Exploration Vehicle (CEV) or other payload to LEO in support of the lunar and Mars missions. The Ares I must successfully provide this capability within cost and schedule, and with an acceptable risk approach. This paper will describe the systems engineering management processes that will be applied to assure Ares I Project success through complete and efficient technical integration. Discussion of technical review and management processes for requirements development and verification, integrated design and analysis, integrated simulation and testing, and the integration of reliability, maintainability and supportability (RMS) into the design will also be included. The Ares I Project is logically divided into elements by the major hardware groupings, and associated management, system engineering, and integration functions. The processes to be described herein are designed to integrate within these Ares I elements and among the other Constellation projects. Also discussed is launch vehicle stack integration (Ares I to CEV, and Ground and Flight Operations integration) throughout the life cycle, including integrated vehicle performance through orbital insertion, recovery of the first stage, and reentry of the upper stage. The processes for decomposing requirements to the elements and ensuring that requirements have been correctly validated, decomposed, and allocated, and that the verification requirements are properly defined to ensure that the system design meets requirements, will be discussed.

Taylor, James L.↗
NASA NTRS2016

Dynamic Systems Analysis for Turbine Based Aero Propulsion Systems

The aircraft engine design process seeks to optimize the overall system-level performance, weight, and cost for a given concept. Steady-state simulations and data are used to identify trade-offs that should be balanced to optimize the system in a process known as systems analysis. These systems analysis simulations and data may not adequately capture the true performance trade-offs that exist during transient operation. Dynamic systems analysis provides the capability for assessing the dynamic tradeoffs at an earlier stage of the engine design process. The dynamic systems analysis concept, developed tools, and potential benefit are presented in this paper. To provide this capability, the Tool for Turbine Engine Closed-loop Transient Analysis (TTECTrA) was developed to provide the user with an estimate of the closed-loop performance (response time) and operability (high pressure compressor surge margin) for a given engine design and set of control design requirements. TTECTrA along with engine deterioration information, can be used to develop a more generic relationship between performance and operability that can impact the engine design constraints and potentially lead to a more efficient engine.

dynamic analysis↗
NASA NTRS2010

The ICARE Method

The ICARE method is a flexible, widely applicable method for systems engineers to solve problems and resolve issues in a complete and comprehensive manner. The method can be tailored by diverse users for direct application to their function (e.g. system integrators, design engineers, technical discipline leads, analysts, etc.). The clever acronym, ICARE, instills the attitude of accountability, safety, technical rigor and engagement in the problem resolution: Identify, Communicate, Assess, Report, Execute (ICARE). This method was developed through observation of Space Shuttle Propulsion Systems Engineering and Integration (PSE&I) office personnel approach in an attempt to succinctly describe the actions of an effective systems engineer. Additionally it evolved from an effort to make a broadly-defined checklist for a PSE&I worker to perform their responsibilities in an iterative and recursive manner. The National Aeronautics and Space Administration (NASA) Systems Engineering Handbook states, engineering of NASA systems requires a systematic and disciplined set of processes that are applied recursively and iteratively for the design, development, operation, maintenance, and closeout of systems throughout the life cycle of the programs and projects. ICARE is a method that can be applied within the boundaries and requirements of NASA s systems engineering set of processes to provide an elevated sense of duty and responsibility to crew and vehicle safety. The importance of a disciplined set of processes and a safety-conscious mindset increases with the complexity of the system. Moreover, the larger the system and the larger the workforce, the more important it is to encourage the usage of the ICARE method as widely as possible. According to the NASA Systems Engineering Handbook, elements of a system can include people, hardware, software, facilities, policies and documents; all things required to produce system-level results, qualities, properties, characteristics, functions, behavior and performance. The ICARE method can be used to improve all elements of a system and, consequently, the system-level functional, physical and operational performance. Even though ICARE was specifically designed for a systems engineer, any person whose job is to examine another person, product, or process can use the ICARE method to improve effectiveness, implementation, usefulness, value, capability, efficiency, integration, design, and/or marketability. This paper provides the details of the ICARE method, emphasizing the method s application to systems engineering. In addition, a sample of other, non-systems engineering applications are briefly discussed to demonstrate how ICARE can be tailored to a variety of diverse jobs (from project management to parenting).

Henke, Luke↗
NASA NTRSDate not supplied

MIKA: Manager for Intelligent Knowledge Access Toolkit for Engineering Knowledge Discovery and Information Retrieval

Repositories of safety reports are often underutilized and only analyzed manually by trained experts, despite safety management systems requiring reports. These collections of documents contain a wealth of information from past projects and operations that could improve system safety and design. Advances in natural language processing techniques have improved information extraction and retrieval in consumer technology, biomedicine, and finance, for instance, but have not been applied to engineering documents on the same scale. To this end, the Manager for Intelligent Knowledge Access (MIKA) open-source toolkit has been developed for rapid knowledge discovery and information retrieval in safety engineering applications. The MIKA toolkit uses state-of-the-art natural language processing algorithms and allows a user to apply these methods to their own dataset. This paper describes the MIKA toolkit and its two primary capabilities, knowledge discovery and information retrieval, and demonstrates the toolkit via a case study on National Transportation Safety Board (NTSB) reports.

Machine Learning↗
NASA NTRS2015

Human Systems Integration (HSI) Practitioner's Guide

The NASA/SP-2015-3709, Human Systems Integration (HSI) Practitioner's Guide, also known as the "HSIPG," provides a tool for implementing HSI activities within the NASA systems engineering framework. The HSIPG is written to aid the HSI practitioner engaged in a program or project (P/P), and serves as a knowledge base to allow the practitioner to step into an HSI lead or team member role for NASA missions. Additionally, this HSIPG is written to address the role of HSI in the P/P management and systems engineering communities and aid their understanding of the value added by incorporating good HSI practices into their programs and projects. Through helping to build a community of knowledgeable HSI practitioners, this document also hopes to build advocacy across the Agency for establishing strong, consistent HSI policies and practices. Human Systems Integration (HSI) has been successfully adopted (and adapted) by several federal agencies-most notably the U.S. Department of Defense (DoD) and the Nuclear Regulatory Commission (NRC)-as a methodology for reducing system life cycle costs (LCCs). These cost savings manifest themselves due to reductions in required numbers of personnel, the practice of human-centered design, decreased reliance on specialized skills for operations, shortened training time, efficient logistics and maintenance, and fewer safety-related risks and mishaps due to unintended human/system interactions. The HSI process for NASA establishes how cost savings and mission success can be realized through systems engineering. Every program or project has unique attributes. This HSIPG is not intended to provide one-size-fits-all recommendations for HSI implementation. Rather, HSI processes should be tailored to the size, scope, and goals of individual situations. The instructions and processes identified here are best used as a starting point for implementing human-centered system concepts and designs across programs and projects of varying types, including manned and unmanned, human spaceflight, aviation, robotics, and environmental science missions. The practitioner using this guide should have expertise in Systems Engineering or other disciplines involved in producing systems with anticipated human interactions. (See section 1.6 of this guide for further discussion on HSI discipline domains.) The HSIPG provides an "HSI layer" to the NASA Systems Engineering Engine (SEE), detailed in NASA Procedural Requirement (NPR) 7123.1B, NASA Systems Engineering Processes and Requirements, and further explained in NASA/SP-2007-6105, Systems Engineering Handbook (see HSIPG Table 2.2-1, NASA Documents with HSI Content, for specific references and document versions).

Zumbado, Jennifer Rochlis↗
DOE OSTI2022

Reinforcement learning for online adaptation of model predictive controllers: Application to a selective catalytic reduction unit

Here we present a novel application of reinforcement learning (RL) for online dynamic tuning of model predictive controllers (MPC). Applying a state-action-reward-state-action (SARSA) algorithm for temporal difference learning with a control-specific reward function improves the error tracking performance of a standard MPC formulation. The proposed RL approach is also readily adaptable to other MPCs, or entirely different control approaches. Practical details for the implementation of the RL-MPC algorithm are also presented. The proposed algorithm is applied to a case study of controlling nitrogen oxide (NO x ) emissions in an industrial selective catalytic reduction (SCR) unit, a control problem characterized by significant nonlinearity and time delay. Along with an RL-MPC formulation for NOx control, another MPC is proposed to mitigate ammonia slip and decrease ammonia consumption in the SCR. Results showing the efficacy of the RL-MPC for NO x control through learning and implementation on the nonlinear SCR dynamic model are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗