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Paper for presentation at The 14th International Symposium on Process Systems Engineering (PSE 2021+), Kyoto, Japan, June 19-23, (2022).
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Slides from seminar to the Texas Water Energy Institute on July 30, 2021
Systems engineering today faces a wide array of challenges, ranging from new operational environments to disruptive technological — necessitating approaches to improve research and development (R&D) efforts. Yet, emphasizing the Aristotelian argument that the “whole is greater than the sum of its parts” seems to offer a conceptual foundation creating new R&D solutions. Invoking systems theoretic concepts of emergence and hierarchy and analytic characteristics of traceability, rigor, and comprehensiveness is potentially beneficial for guiding R&D strategy and development to bridge the gap between theoretical problem spaces and engineering-based solutions. In response, this article describes systems–theoretic process analysis (STPA) as an example of one such approach to aid in early-systems R&D discussions. STPA—a ‘top-down’ process that abstracts real complex system operations into hierarchical control structures, functional control loops, and control actions—uses control loop logic to analyze how control actions (designed for desired system behaviors) may become violated and drive the complex system toward states of higher risk. By analyzing how needed controls are not provided (or out of sequence or stopped too soon) and unneeded controls are provided (or engaged too long), STPA can help early-system R&D discussions by exploring how requirements and desired actions interact to either mitigate or potentially increase states of risk that can lead to unacceptable losses. This article will demonstrate STPA's benefit for early-system R&D strategy and development discussion by describing such diverse use cases as cyber security, nuclear fuel transportation, and US electric grid performance. Together, the traceability, rigor, and comprehensiveness of STPA serve as useful tools for improving R&D strategy and development discussions. In conclusion, leveraging STPA as well as related systems engineering techniques can be helpful in early R&D planning and strategy development to better triangulate deeper theoretical meaning or evaluate empirical results to better inform systems engineering solutions.
Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.
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.
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.
Abstract A physical understanding of the progression from flow‐driven (quasi‐static) poromechanical deformation to dynamic fault rupture is critical to the resilient operations of several engineering systems. These processes are bridged by a progression from fault reactivation to the spontaneous nucleation of unstable sliding. Toward addressing this challenge, novel automatic time step size control methods are developed to enable accurate and efficient simulation of these dynamics and transitions from the first principles. The controllers combine local models for discretization error and Coulomb failure conditions to automatically adjust the time step size across several orders of magnitude. The methods do not require additional empirical or theoretical input and can resolve the pre‐rupture, interseismic, and seismic periods to the allowed accuracy. The computational results reveal that the proposed methods automatically capture the onset of reactivation and nucleation for homogeneous and heterogeneous fields. Hydrodynamic and structural heterogeneity lead to disparate critical nucleation sizes compared to those predicted by theory. The results highlight its potential in predicting induced seismicity in realistic subsurface engineering systems and at practical scales.
The combustion of middle distillates (diesel and jet fuel) is responsible for the emission of more than 2 GTon of CO 2 per year worldwide. While sustainable alternatives exist for gasoline and jet fuel, we still lack sustainable alternatives for diesel. This fact is especially relevant if we consider that electrification of sectors where diesel is used is challenging. One sustainable approach for diesel production is the catalytic upgrading of ethanol. While most work in this field has focused on the dehydration/oligomerization of ethanol, this approach is limited to producing fuels that have a high degree of branching and low cetane number. Another approach is the sequential use of ethanol Guerbet coupling, leading to higher alcohols, followed by etherification, leading to large ethers which, importantly, result in a product with high cetane number. In this work, we explore the catalytic upgrading of ethanol into diesel following an approach based on the initial Guerbet coupling of ethanol followed by etherification. The results presented are a collaborative and synergic effort among process and systems engineers, experimentalist in the area of catalysis, and fuel property modelers. Here, we demonstrate experimentally the feasibility of upgrading ethanol into a diesel fuel with properties that surpass its fossil counterpart. The diesel produced has a predicted cetane number of ~70 and outstanding cold flow properties, while maintaining other properties (viscosity, density, and flash point) within expected ranges. A technoeconomic analysis performed based on a detailed biorefinery model shows that the MFSP is ~5.89$ per Gal in 2021 dollars when lignocellulosic ethanol is used, with the most relevant economic driver the cost of the ethanol feedstock. The upgrading process can be performed with a net energy gain (EROI = 1.49 > 1). An LCA analysis of greenhouse gas (GHG) emissions reveals that the use of lignocellulosic ethanol may lead to more than 50% reduction in GHG emissions compared to fossil diesel. Depending on the CO 2 emissions associated with the production of ethanol we show that in some instances the production of carbon neutral diesel fuel is possible.
This is a standalone release of PARETO UI using the previously released PARETO version 0.9.0 for the backend.. New features in this version of PARETO UI are: - Added functionality to upload GIS map network visualizations and auto generate input templates - Added map visualizations based on GIS data
PARETO 0.9.0 Release. Highlights: New Features - Initial beneficial reuse implementation - Post-optimization timing for infrastructure buildout Bug Fixes - Address Pyomo solver bug for UI Gurobi solve - Update Toy Case Study to feasible data for hydraulics post_process - Update Jupyter notebook for fall release UI Updates - Added functionality to optimize with hydraulics options - Added new plots to KPI dashboard for water quality and hydraulics timelines
PARETO 0.8.0 Release. Highlights: Model Updates - Applied unified sets for pipeline and trucking arcs in strategic model - Apply unified sets for pipeline and trucking arcs in operational model - Added new config argument for removal efficiency calculation method - Standardized bidirectional capacity constraint - Added dependencies removed in IDAES 2.1 - Created bounding functions & utilities - Added Hydraulics module to the strategic model - Add additional arc types to strategic model Documentation and Tutorial Updates - Improved PARETO treatment document - Introduced general tutorial and treatment module Jupyter notebooks for Strategic Model - Update docs with correct support email list address - Consolidate and deduplicate Getting Started and resources for developers - Enable Black formatting for Jupyter notebooks - Add Binder configuration files and README Bug Fixes - Fix strategic model documentation typos - Removed duplicated units from output file header UI Updates - Added view for comparing different scenarios - Added functionality for manually overriding PARETO decisions
PARETO 0.7.0 Release. Highlights: - Update years for copyright - Correct Core-dev installation instructions - Improve delivery constraint indexing - Incorporate component removal efficiency at treatment sites - Allow model Parameters to be mutable - Address CodeCov failures - Ensure compatibility with IDAES v2
PARETO 0.5.0 Release. Highlights: Treatment Center Modeling - New case study with desalination, clean brine, and evaporation treatment technologies - New option to reduce disposal capacity due to Seismic Response Areas (SRA) - New option to consider water sharing outside of system General Updates - New features for Sankey Diagram visualization (multiple regions, filtered time periods) - Introduce badges to README.md - Convert documentation to ASCII LaTeX - Code cleaning and maintenance - Correct strategic model documentation typo - Update and format treatment demo input spreadsheet Bug Fixes - Fix water quality operational model results printing bug - Piping and trucking variables are now built only over defined arcs instead of all possible connections - Revise pipeline expansion cost constraints, ensure that all pipelines built incur cost
PARETO 0.3.0 Release. Highlights: - Fixing Warning in get_data - Clean up of old main function which has been replaced by the run files - Adding plot_scatter() function - Support node capacity - Add Gurobi troubleshooting files to gitignore by @melody-shellman - Set slacks to zero - Water treatment case study - Fix defaultvalue treatement - Fix bug for incorrect number of input arguments in water quality - Pipeline configs documentation - Enable rendering Unicode characters in ReadTheDocs PDF builds - Adding support for units - Add discrete water quality to the model - Updating documentation for strategic model, and corresponding fixes to strategic model - Rework getting started docs build section and more - Enable Codecov reports in CI builds - Correct error in CompletionsPadSupplyBalanceRule
First official software release for Project PARETO..
The Water treatment Technoeconomic Assessment Platform (WaterTAP) is an open-source Python-based software package that supports the simulation and optimization of process-scale water treatment trains. WaterTAP seeks to provide the broader water research community with an integrated modeling capability to evaluate cost, energy, and environmental tradeoffs across water treatment options and identify high impact opportunities for innovation including novel materials, processes, and systems. An updated version of WaterTAP is released quarterly and each includes documentation and release notes.