Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Cleaning strategies and cost modelling of experimental membrane-based desalination plants
    ( 2021)
    Karimanzira, Divas
    ;
    Went, Joachim
    ;
    Neumann, Frank
    In Project WASTEC, an experimental Reverse Osmosis (RO) desalination system was developed. It serves as a platform for testing new technologies. For this system, we solved two problems, which are described in this paper. Firstly, we developed and investigated strategies for scheduling chemical enhanced backwashing and chemical cleaning and secondly, due to the experimental nature of the project, several new technological developments with respect to materials and methods were integrated into the system and requires tools for evaluating the economic viability of the new technologies. In this task, the economics of membrane-based desalination will be investigated. Baseline systems of reverse osmosis and pretreatment systems (microfiltration and ultrafiltration) will be economically examined and compared for their investments and operational costs. Sensitivity of the different plant and membrane parameters to the cost will be studied. Results show that with respect to costs, for a 200m3/hr design capacity plant, a volume of water is produced by a MF process at a cost of $0.494 and at a cost of $0.486 by an ultrafiltration process microfiltration. The reverse osmosis process cannot be compared directly, but it required $ 0.49 / m3 for a plant with 56 m3/hour design capacity. The values are in line with the costs reported in literature for membrane-based filtration.
  • Publication
    Performance Prediction of a Reverse Osmosis Desalination System Using Machine Learning
    ( 2021)
    Karimanzira, Divas
    ;
    Rauschenbach, Thomas
    One of the major challenges that membrane manufacturers, commercial enterprises and the scientific community in the field of membrane-based filtration or reverse osmosis (RO) desalination have to deal with is system performance retardation due to membrane fouling. In this respect, the prediction of fouling or system performance in membrane-based systems is the key to determining the mid and long-term plant operating conditions and costs. Despite major research efforts in the field, effective methods for the estimation of fouling in RO desalination plants are still in infancy, for example, most of the existing methods, neither consider the characteristics of the membranes such as the spacer geometry, nor the efficiency and the day to day chemical cleanings. Furthermore, most studies focus on predicting a single fouling indicator, e.g., flux decline. Faced with the limits of mathematical or numerical approach, in this paper, machine learning methods based on Multivariate Temporal Convolutional Neural networks (MTCN), which take into account the membrane characteristics, feed water quality, RO operation data and management practice such as Cleaning In Place (CIP) will be considered to predict membrane fouling using measurable multiple indicators. The temporal convolution model offers one the capability to explore the temporal dependencies among a remarkably long historical period and has potential use for operational diagnostics, early warning and system optimal control. Data collected from a Desalination RO plant will be used to demonstrate the capabilities of the prediction system. The method achieves remarkable predictive accuracy (root mean square error) of 0.023, 0.012 and 0.007 for the relative differential pressure and permeates Total Dissolved solids (TDS) and the feed pressure, respectively.
  • Publication
    An intelligent management system for aquaponics
    ( 2021)
    Karimanzira, Divas
    ;
    Rauschenbach, Thomas
    Population rise, climate change, soil degradation, water scarcity, and food security require efficient and sustainable food production. Aquaponics is a highly efficient way of farming and is becoming increasingly popular. However, large scale aquaponics still lack stability, standardization and proof of economical profitability. The EU-INAPRO project helps to overcome these limitations by introducing digitization, enhanced technology, and developing standardized modular scalable solutions and demonstrating the viability of large aquaponics. INAPRO is based on an innovation a double water recirculation system (DRAPS), one for fish, and the other one for crops. In DRAPS, optimum conditions can be set up individually for fish and crops to increase productivity of both. Moreover, the integration of digital technologies and data management in the aquaculture production and processing systems will enable full traceability and transparency in the processes, increasing consumers' trust in aquaculture products. In this paper, the innovations and the digitization approach will be introduced and explained and the key benefits of the system will be emphasized.
  • Publication
    Deep Learning for Long and Short Range Object Detection in Underwater Environment
    ( 2021)
    Karimanzira, Divas
    ;
    Pfützenreuter, Torsten
    ;
    Renkewitz, Helge
    Docking capabilities are required for long operation of an Autonomous Underwater Vehicle (AUV) due to limited battery and data storage capacity. Docking is composed of four main processes, i.e. homing, plug-in, release and drive-out. The homing part requires detection and localization of the docking station, guidance and control to reach the docking station. Hence, in this paper, we focus on the process of detection and localization. The AUV is assumed to use two sensors for perception of the environment, i.e. an imaging sonar and a monocular camera which is used in close range navigation. Deep learning (DeepL) methods are well-known for good detection and localization. Therefore, in this paper, two DeepL networks will be designed. The first one is for detecting the target object in a sonar image at far range and the other one is for detection of the docking station in the close range using data from an optical camera image. The results from experimental studies in a test basin with an AUV show that the proposed system is able to locate and classify the docking station in both optical and sonar images with detection rate of 94.3% and 80%, respectively.