SummaryConstructed wetlands have been used for decades on industrial areas to treat stormwater. European regulations and local ambitions for water quality dictate lower emissions before the water is discharged to the drainage system, surface water or infiltrated to ground water. The increase in the required removal efficiency requires a better understanding of the characteristics of pollutants and cost-effective performance of constructed wetlands. In this chapter detailed characteristics of stormwater from (industrial) areas is given together with monitored removal efficiencies and the cost of constructed wetlands. Some case studies with constructed wetlands are selected and reviewed in this chapter which can be regarded as Best Management Practices (BMPs). In most cases the constructed wetlands are not monitored in detail but perceived to be effective. Long-term performance, however, remains an issue. New monitoring techniques such as underwater drones and full scale testing can be applied to get new insights on optimizing the hydraulic capacity and removal efficiency of wetlands. Last but not least: international knowledge exchange on constructed wetlands and new monitoring techniques can be promoted by interactive online tools.
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Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.
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Symbiotic Urban Agriculture Networks (SUANs) are a specific class of symbiotic networks that intend to close material and energy loops from cities and urban agriculture. Private and public stakeholders in SUANs face difficulties in the implementation of technological and organisational design interventions due to the complex nature of the agricultural and urban environment. Current research on the dynamics of symbiotic networks, especially Industrial Symbiosis (IS), is based on historical data from practice, and provides only partly for an understanding of symbiotic networks as a sociotechnical complex adaptive system. By adding theory and methodology from Design Science, participatory methods, and by using agent-based modelling as a tool, prescriptive knowledge is developed in the form of grounded and tested design rules for SUANs. In this paper, we propose a conceptual Design Science method with the aim to develop an empirically validated participatory agent-based modelling strategy that guides sociotechnical design interventions in SUANs. In addition, we present a research agenda for further strategy, design intervention, and model development through case studies regarding SUANs. The research agenda complements the existing analytical work by adding a necessary Design Science approach, which contributes to bridging the gap between IS dynamics theory and practical complex design issues.
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Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology which uses strong electric fields to manipulate liquid atomization. Among many other areas, electrospray is currently used as an important tool for biomedical applications (droplet encapsulation), water technology (thermal desalination and metal recovery) and material sciences (nanofibers and nano spheres fabrication, metal recovery, selective membranes and batteries). A complete review about the particularities of this technology and its applications was recently published in a special edition of the Journal of Aerosol Sciences [1]. Even though EHDA is already applied in many different industrial processes, there are not many controlling tools commercially available which can be used to remotely operate the system as well as identify some spray characteristics, e.g. droplet size, operational mode, droplet production ratio. The AECTion project proposes the development of an innovative controlling system based on the electrospray current, signal processing & control and artificial intelligence to build a non-visual tool to control and characterize EHDA processes.
Drones have been verified as the camera of 2024 due to the enormous exponential growth in terms of the relevant technologies and applications such as smart agriculture, transportation, inspection, logistics, surveillance and interaction. Therefore, the commercial solutions to deploy drones in different working places have become a crucial demand for companies. Warehouses are one of the most promising industrial domains to utilize drones to automate different operations such as inventory scanning, goods transportation to the delivery lines, area monitoring on demand and so on. On the other hands, deploying drones (or even mobile robots) in such challenging environment needs to enable accurate state estimation in terms of position and orientation to allow autonomous navigation. This is because GPS signals are not available in warehouses due to the obstruction by the closed-sky areas and the signal deflection by structures. Vision-based positioning systems are the most promising techniques to achieve reliable position estimation in indoor environments. This is because of using low-cost sensors (cameras), the utilization of dense environmental features and the possibilities to operate in indoor/outdoor areas. Therefore, this proposal aims to address a crucial question for industrial applications with our industrial partners to explore limitations and develop solutions towards robust state estimation of drones in challenging environments such as warehouses and greenhouses. The results of this project will be used as the baseline to develop other navigation technologies towards full autonomous deployment of drones such as mapping, localization, docking and maneuvering to safely deploy drones in GPS-denied areas.
Deploying robots from indoor to outdoor environments (vise versa) with stable and accurate localization is very important for companies to secure the utilization in industrial applications such as delivering harvested fruits from plantations, deploying/docking, navigating under solar panels, passing through tunnels/underpasses and parking in garages. This is because of the sudden changes in operational conditions such as receiving high/low-quality satellite signals, changing field of view, dealing with lighting conditions and addressing different velocities. We observed these limitations especially in indoor-outdoor transitions after conducting different projects with companies and obtaining inaccurate localization using individual Robotics Operating Systems (ROS2) modules. As there are rare commercial solutions for IO-transitions, AlFusIOn is a ROS2-based framework aims to fuse different sensing and data-interpretation techniques (LiDAR, Camera, IMU, GNSS-RTK, Wheel Odometry, Visual Odometry) to guarantee the redundancy and accuracy of the localization system. Moreover, maps will be integrated to robustify the performance and ensure safety by providing geometrical information about the transitioning structures. Furthermore, deep learning will be utilized to understand the operational conditions by labeling indoor and outdoor areas. This information will be encoded in maps to provide robots with expected operational conditions in advance and beyond the current sensing state. Accordingly, this self-awareness capability will be incorporated into the fusion process to control and switch between the localization techniques to achieve accurate and smooth IO-transitions, e.g., GNSS-RTK will be deactivated during the transition. As an urgent and unique demand to have an accurate and continuous IO-transition towards fully autonomous navigation/transportation, Saxion University and the proposal’s partners are determined to design a commercial and modular industrial-based localization system with robust performance, self-awareness about the localization capabilities and less human interference. Furthermore, AlFusIOn will intensively collaborate with MAPS (a RAAKPRO proposed by HAN University) to achieve accurate localization in outdoor environments.
