The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Monitoring and detecting faults in wastewater treatment plants using deep learning

Wastewater treatment plants use many sensors to control energy consumption and discharge quality. These sensors produce a vast amount of data which can be efficiently monitored by automatic systems. Consequently, several different statistical and learning methods are proposed in the literature which can automatically detect faults. While these methods have shown promising results, the nonlinear dynamics and complex interactions of the variables in wastewater data necessitate more powerful methods with higher learning capacities. In response, this study focusses on modelling faults in the oxidation and nitrification process. Specifically, this study investigates a method based on deep neural networks (specifically, long short-term memory) compared with statistical and traditional machine-learning methods. The network is specifically designed to capture temporal behaviour of sensor data. The proposed method is evaluated on a real-life dataset containing over 5.1 million sensor data points. The method achieved a fault detection rate (recall) of over 92%, thus outperforming traditional methods and enabling timely detection of collective faults

Influent generator : towards realistic modelling of wastewater flowrate and water quality using machine-learning methods

Depuis que l'assainissement des eaux usées est reconnu comme un des objectifs de développement durable des Nations Unies, le traitement et la gestion des eaux usées sont devenus plus importants que jamais. La modélisation et la digitalisation des stations de récupération des ressources de l'eau (StaRRE) jouent un rôle important depuis des décennies, cependant, le manque de données disponibles sur les affluents entrave le développement de la modélisation de StaRRE. Cette thèse vis e à faire progresser la modélisation des systèmes d'assainissement en général, et en particulier en ce qui concerne la génération dynamique des affluents. Dans cette étude, différents générateurs d'affluent (GA), qui peuvent fournir un profil d'affluent dynamique, ont été proposés, optimisés et discutés. Les GA développés ne se concentrent pas seulement sur le débit, les solides en suspension et la matière organique, mais également sur les substances nutritives telles que l'azote et le phosphore. En outre, cette étude vise à adapter les GA à différentes applications en fonction des différentes exigences de modélisation. Afin d'évaluer les performances des GA d'un point de vue général, une série de critères d'évaluation de la qualité du modèle est décrite. Premièrement, pour comprendre la dynamique des affluents, une procédure de caractérisation des affluents a été développée et testée pour une étude de cas à l'échelle pilote. Ensuite, pour générer différentes séries temporelles d'affluent, un premier GA a été développé. La méthodologie de modélisation est basée sur l'apprentissage automatique en raison de ses calculs rapides, de sa précision et de sa capacité à traiter les mégadonnées. De plus, diverses versions de ce GA ont été appliquées pour différents cas optimisées en fonction des disponibilités d'études et ont été des données (la fréquence et l'horizon temporel), des objectifs et des exigences de précision. Les résultats démontrent que : i) le modèle GA proposé peut être utilisé pour générer d'affluents dynamiques réalistes pour différents objectifs, et les séries temporelles résultantes incluent à la fois le débit et la concentration de polluants avec une bonne précision et distribution statistique; ii) les GA sont flexibles, ce qui permet de les améliorer selon différents objectifs d'optimisation; iii) les GA ont été développés en considérant l'équilibre entre les efforts de modélisation, la collecte de données requise et les performances du modèle. Basé sur les perspectives de modélisation des StaRRE, l'analyse des procédés et la modélisation prévisionnelle, les modèles de GA dynamiques peuvent fournir aux concepteurs et aux modélisateurs un profil d'affluent complet et réaliste, ce qui permet de surmonter les obstacles liés au manque de données d'affluent. Par conséquent, cette étude a démontré l'utilité des GA et a fait avancer la modélisation des StaRRE en focalisant sur l'application de méthodologies d'exploration de données et d'apprentissage automatique. Les GA peuvent donc être utilisés comme outil puissant pour la modélisation des StaRRE, avec des applications pour l'amélioration de la configuration de traitement, la conception de procédés, ainsi que la gestion et la prise de décision stratégique. Les GA peuvent ainsi contribuer au développement de jumeaux numériques pour les StaRRE, soit des système intelligent et automatisé de décision et de contrôle.Since wastewater sanitation is acknowledged as one of the sustainable development goals of the United Nations, wastewater treatment and management have been more important then ever. Water Resource Recovery Facility (WRRF) modelling and digitalization have been playing an important role since decades, however, the lack of available influent data still hampers WRRF model development. This dissertation aims at advancing the field of wastewater systems modelling in general, and in particular with respect to the dynamic influent generation. In this study, different WRRF influent generators (IG), that can provide a dynamic influent flow and pollutant concentration profile, have been proposed, optimized and discussed. The developed IGs are not only focusing on flowrate, suspended solids, and organic matter, but also on nutrients such as nitrogen and phosphorus. The study further aimed at adapting the IGs to different case studies, so that future users feel comfortable to apply different IG versions according to different modelling requirements. In order to evaluate the IG performance from a general perspective, a series of criteria for evaluating the model quality were evaluated. Firstly, to understand the influent dynamics, a procedure of influent characterization has been developed and experimented at pilot scale. Then, to generate different realizations of the influent time series, the first IG was developed and a data-driven modelling approach chosen, because of its fast calculations, its precision and its capacity of handling big data. Furthermore, different realizations of IGs were applied to different case studies and were optimized for different data availabilities (frequency and time horizon), objectives, and modelling precision requirements. The overall results indicate that: i) the proposed IG model can be used to generate realistic dynamic influent time series for different case studies, including both flowrate and pollutant concentrations with good precision and statistical distribution; ii) the proposed IG is flexible and can be improved for different optimization objectives; iii) the IG model has been developed by considering the balance between modelling efforts, data collection requirements and model performance. Based on future perspectives of WRRF process modelling, process analysis, and forecasting, the dynamic IG model can provide designers and modellers with a complete and realistic influent profile and this overcomes the often-occurring barrier of shortage of influent data for modelling. Therefore, this study demonstrated the IGs' usefulness for advanced WRRF modelling focusing on the application of data mining and machine learning methodologies. It is expected to be widely used as a powerful tool for WRRF modelling, improving treatment configurations and process designs, management and strategic decision-making, such as when transforming a conventional WRRF to a digital twin that can be used as an intelligent and automated system

Cyber-Physical Systems for Smart Water Networks: A Review

There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.acceptedVersio

Monitoring the waste to energy plant using the latest AI methods and tools

Solid wastes for instance, municipal and industrial wastes present great environmental concerns and challenges all over the world. This has led to development of innovative waste-to-energy process technologies capable of handling different waste materials in a more sustainable and energy efficient manner. However, like in many other complex industrial process operations, waste-to-energy plants would require sophisticated process monitoring systems in order to realize very high overall plant efficiencies. Conventional data-driven statistical methods which include principal component analysis, partial least squares, multivariable linear regression and so forth, are normally applied in process monitoring. But recently, latest artificial intelligence (AI) methods in particular deep learning algorithms have demostrated remarkable performances in several important areas such as machine vision, natural language processing and pattern recognition. The new AI algorithms have gained increasing attention from the process industrial applications for instance in areas such as predictive product quality control and machine health monitoring. Moreover, the availability of big-data processing tools and cloud computing technologies further support the use of deep learning based algorithms for process monitoring. In this work, a process monitoring scheme based on the state-of-the-art artificial intelligence methods and cloud computing platforms is proposed for a waste-to-energy industrial use case. The monitoring scheme supports use of latest AI methods, laveraging big-data processing tools and taking advantage of available cloud computing platforms. Deep learning algorithms are able to describe non-linear, dynamic and high demensionality systems better than most conventional data-based process monitoring methods. Moreover, deep learning based methods are best suited for big-data analytics unlike traditional statistical machine learning methods which are less efficient. Furthermore, the proposed monitoring scheme emphasizes real-time process monitoring in addition to offline data analysis. To achieve this the monitoring scheme proposes use of big-data analytics software frameworks and tools such as Microsoft Azure stream analytics, Apache storm, Apache Spark, Hadoop and many others. The availability of open source in addition to proprietary cloud computing platforms, AI and big-data software tools, all support the realization of the proposed monitoring scheme

Distributed environmental monitoring

With increasingly ubiquitous use of web-based technologies in society today, autonomous sensor networks represent the future in large-scale information acquisition for applications ranging from environmental monitoring to in vivo sensing. This chapter presents a range of on-going projects with an emphasis on environmental sensing; relevant literature pertaining to sensor networks is reviewed, validated sensing applications are described and the contribution of high-resolution temporal data to better decision-making is discussed

Process control of a laboratory combustor using neural networks

Active feedback and feedforward-feedback control systems based on static-trained feedforward multi-layer-perceptron (FMLP) neural networks were designed and demonstrated, by experiment and simulation, for selected species from a laboratory two stage combustor. These virtual controllers functioned through a Visual Basic platform. A proportional neural network controller (PNNC) was developed for a monotonic control problem - the variation of outlet oxygen level with overall equivalence ratio (Φ0). The FMLP neural network maps the control variable to the manipulated variable. This information is in turn transferred to a proportional controller, through the variable control bias value. The proposed feedback control methodology is robust and effective to improve control performance of the conventional control system without drastic changes in the control structure. A detailed case study in which two clusters of FMLP neural networks were applied to a non-monotonic control problem - the variation of outlet nitric oxide level with first-stage equivalence ratio (Φ0) - was demonstrated. The two clusters were used in the feedforward-feedback control scheme. The key novelty is the functionalities of these two network clusters. The first cluster is a neural network-based model-predictive controller (NMPC). It identifies the process disturbance and adjusts the manipulated variables. The second cluster is a neural network-based Smith time-delay compensator (NSTC) and is used to reduce the impact of the long sampling/analysis lags in the process. Unlike other neural network controllers reported in the control field, NMPC and NSTC are efficiently simple in terms of the network structure and training algorithm. With the pre-filtered steady-state training data, the neural networks converged rapidly. The network transient response was originally designed and enabled here using additional tools \u27and mathematical functions in the Visual Basic program. The controller based on NMPC/NSTC showed a superior performance over the conventional proportional-integral derivative (PID) controller. The control systems developed in this study are not limited to the combustion process. With sufficient steady-state training data, the proposed control systems can be applied to control applications in other engineering fields