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  • 1.
    Benavente, Verónica
    et al.
    RISE Research Institutes of Sweden, Bioeconomy and Health, Biorefinery and Energy. Umeå university, Sweden.
    Pérez, Carla
    Umeå university, Sweden.
    Jansson, Stina
    Umeå university, Sweden.
    Co-hydrothermal carbonization of microalgae and digested sewage sludge: Assessing the impact of mixing ratios on the composition of primary and secondary char2024In: Waste Management, ISSN 0956-053X, E-ISSN 1879-2456, Vol. 174, p. 429-438Article in journal (Refereed)
    Abstract [en]

    The role of microalgae cultivation in wastewater treatment and reclamation has been studied extensively, as has the potential utility of the resulting algal biomass. Most methods for processing such biomass generate solid residues that must be properly managed to comply with current sustainable resource utilization requirements. Hydrothermal carbonization (HTC) can be used to process both individual wet feedstocks and mixed feedstocks (i.e., co-HTC). Here, we investigate co-HTC using microalgae and digested sewage sludge as feedstocks. The objectives were to (i) study the material’s partitioning into solid and liquid products, and (ii) characterize the products’ physicochemical properties. Co-HTC experiments were conducted at 180–250°C using mixed microalgae/sewage sludge feedstocks with the proportion of sewage sludge ranging from 0 to 100 %. Analyses of the hydrochar composition and the formation and composition of secondary char revealed that the content of carbonized material in the product decreased as the proportion of sewage sludge in the feedstock increased under fixed carbonization conditions. The properties of the hydrochars and the partitioning of material between the liquid phase and the hydrochar correlated linearly with the proportion of microalgae in mixed feedstocks, indicating that adding sewage sludge to microalgae had weak or non-existent synergistic effects on co-HTC outcomes. However, the proportion of sewage sludge in the feedstock did affect the secondary char. For example, adding sewage sludge reduced the abundance of carboxylic acids and ketones as well as the concentrations of higher molecular weight cholesterols. Such changes may alter the viable applications of the hydrochar. 

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  • 2.
    Sun, Jiacheng
    et al.
    University of Edinburgh, UK.
    Benavente, Verónica
    RISE Research Institutes of Sweden, Bioeconomy and Health, Biorefinery and Energy.
    Mašek, Ondřej
    University of Edinburgh, UK.
    Synergistic valorisation of wastewater microalgae: Sulphuric acid-assisted phosphorus recovery and enhanced electrochemical performance of biochar for supercapacitors2024In: Chemical Engineering Journal, ISSN 1385-8947, E-ISSN 1873-3212, Vol. 494, article id 152986Article in journal (Refereed)
    Abstract [en]

    This study proposes a strategy for phosphorus recovery from algae biomass while simultaneously enhancing the electrochemical capacitance of resulting biochar in supercapacitor applications. Use of sulphuric acid leaching process on both wastewater microalgae and seaweed biochar demonstrated high phosphorus recovery rates, ranging from 93.4% to 95.4%, while at the same time retaining 52.7% to 58.6% of Fe content in the biochars. By modifying the sequential order of leaching (L) and physical activation (A) as LA and AL, the strategy allows for the optimisation of phosphorus recovery and the electrochemical properties of activated biochars. During the LA process, a more porous structure formed and more S-containing functional groups occurred compared to AL which explained the higher specific capacitance (486.3F g−1 at a current density of 1 A g−1) of wastewater microalgae biocarbon with the LA process. Consequently, the life cycle assessment of this strategy revealed a significant global warming reduction potential of 1.34–2.94 tonnes of CO2-eq/yr for each tonne of biochar produced. This indicates that sulphuric acid-assisted biochar production could be a sustainable strategy for waste management and carbon sequestration, while simultaneously generating economic profits from valorised carbon material for energy storage applications.

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  • 3.
    Sun, J
    et al.
    University of Edinburgh, UK.
    Benavente, Verónica
    RISE Research Institutes of Sweden, Bioeconomy and Health, Biorefinery and Energy. Umeå University, Sweden.
    Jansson, Stina
    Umeå University, Sweden.
    Mašek, O.
    University of Edinburgh, UK.
    Comparative characterisation and phytotoxicity assessment of biochar and hydrochar derived from municipal wastewater microalgae biomass2023In: Bioresource Technology, ISSN 0960-8524, E-ISSN 1873-2976, Vol. 386, article id 129567Article in journal (Refereed)
    Abstract [en]

    Microalgae, originating from a tertiary treatment of municipal wastewater, is considered a sustainable feedstock for producing biochar and hydrochar, offering great potential for agricultural use due to nutrient content and carbon storage ability. However, there are risks related to contamination and these need to be carefully assessed to ensure safe use of material from wastewater microalgae. Therefore, this study compared the properties and phototoxicity of biochar and hydrochar produced via pyrolysis and hydrothermal carbonisation (HTC) of microalgae under different temperatures and residence times. While biochar promoted germination and seedling growth by up to 11.0% and 70.0%, respectively, raw hydrochar showed strong phytotoxicity, due to the high content of volatile matter. Two post-treatments, dichloromethane (DCM) washing and further pyrolysis, proved to be effective methods for mitigating phytotoxicity of hydrochar. Additionally, biochar had 35.8–38.6% fixed carbon, resulting in higher carbon sequestration potential compared to hydrochar. © 2023 The Author(s)

  • 4.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Ambrus, Rares
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Caccamo, Sergio
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Chen, Xi
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Cruciani, Silvia
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Pinto Basto de Carvalho, Joao Frederico
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Haustein, Joshua
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Marzinotto, Alejandro
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Viña, Francisco
    Karayiannidis, Yiannis
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Ögren, Petter
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Team KTH’s Picking Solution for the Amazon Picking Challenge 20162020In: Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment, Springer Nature , 2020, p. 53-62Chapter in book (Other academic)
    Abstract [en]

    In this chapter we summarize the solution developed by team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition, which simulated a warehouse automation scenario, was divided into two parts: a picking task, where the robot picks items from a shelf and places them into a tote, and a stowing task, where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting with a high-level overview of the system, delving later into the details of our perception pipeline and strategy for manipulation and grasping. The hardware platform used in our solution consists of a Baxter robot equipped with multiple vision sensors.

  • 5.
    Almeida, Diogo
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Ambrus, Rares
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Caccamo, Sergio
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Chen, Xi
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Cruciani, Silvia
    Pinto Basto De Carvalho, Joao F
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Haustein, Joshua
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Marzinotto, Alejandro
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Vina, Francisco
    KTH.
    Karayiannidis, Yiannis
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Ögren, Petter
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Kragic, Danica
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Team KTH’s Picking Solution for the Amazon Picking Challenge 20162017In: Warehouse Picking Automation Workshop 2017: Solutions, Experience, Learnings and Outlook of the Amazon Robotics Challenge, 2017Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    In this work we summarize the solution developed by Team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition simulated a warehouse automation scenario and it was divided in two tasks: a picking task where a robot picks items from a shelf and places them in a tote and a stowing task which is the inverse task where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting from a high level overview of our system and later delving into details of our perception pipeline and our strategy for manipulation and grasping. The solution was implemented using a Baxter robot equipped with additional sensors.

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