Pernajan Gammelbyvikenin hoito- ja käyttösuunnitelma 2005-2014

Pernajanlahden pohjoisin osa, Gammelbyviken, on linnustollisesti ja maisemallisesti arvokas alue, joka kuuluu valtakunnalliseen lintuvesien suojeluohjelmaan ja Natura 2000 -suojelualueverkostoon. Lahdella 1900-luvun jälkipuoliskolla tapahtuneet muutokset ovat olleet alueen maisemakuvan ja luonnon monimuotoisuuden kannalta pääosin haitallisia. Sekä Gammelbyvikenin nykyisten erityisarvojen säilyminen että jo kadonneiden arvojen palauttaminen edellyttävät suunniteltuja hoito- ja kunnostustoimia. Gammelbyviken on melko matala, rehevän ilmaversois- ja kelluslehtiskasvillisuuden sekä ruokoluhtien luonnehtima lahti. Alueella pesii monilajinen ja runsas linnusto, johon kuuluu useita vaateliaita ja harvinaisia lintuvesien lajeja. Pesivistä vesilinnuista runsaslukuisimpia ovat silkkiuikku, sinisorsa ja nokikana. Harvalukuisista lajeista alueella pesivät säännöllisesti ruskosuohaukka, kaulushaikara, kurki, luhtakana, luhtahuitti, ruisrääkkä ja rastaskerttunen. Alueella pesii myös naurulokkiyhdyskunta. Gammelbyvikenin pohjoisosaa reunustavilla laajoilla rantaniityillä pesivät mm. kuovi, töyhtöhyyppä, punajalkaviklo, taivaanvuohi, keltavästäräkki ja pensastasku. Muutonaikana Gammelbyvikenillä levähtää runsaasti vesilintuja, joista runsaimpia ovat laulujoutsen, haapana, tavi, sinisorsa, isokoskelo ja telkkä. Rantaniityillä säännöllisesti levähtäviä kahlaajalajeja ovat mm. liro, valkoviklo ja suokukko. Gammelbyvikenin hoito- ja käyttösuunnitelma on laadittu vuosiksi 2005–2014. Toimenpide-ehdotusten toteuttaminen on aloitettu Lintulahdet Life -projektissa vuonna 2004. Natura-alueen suojelutavoitteiden mukaisesti toimenpiteet kohdistuvat erityisesti kosteikko- ja rantalinnuston elinolojen parantamiseen sekä kulttuuribiotooppien suojeluun ja ennallistamiseen. Myös virkistyskäyttötarpeet sisältyvät suunnitteluun

Electrical Capacitance Tomography to Measure Moisture Distribution of Polymer Foam in a Microwave Drying Process

Moisture distribution information is a critical element in drying processes. The drying of products by employing high-power microwave (MW) technology is widely used in the industry. Although microwaves allow volumetric and selective heating resulting in a significant reduction of processing time and energy consumption, there is always a risk of non-uniform moisture distribution in the final product. This paper investigates the capability of a designed electrical capacitance tomography (ECT) sensor to estimate the moisture distribution of polymer foams in a microwave drying process. The moisture distribution is estimated based on the non-intrusive contactless measurements of the electrical capacitances between the electrodes mounted on a frame around the target polymer foam. The obtained moisture information can be employed as feedback to a controller to adjust the power level of each microwave source in the microwave system to reduce or eliminate the non-homogeneity of the moisture distribution inside the polymer foam. In a series of experiments, we first examine the capability of the ECT sensor in estimating the moisture distribution in a stationary foam. We extend the tests to estimating the moisture distribution in a case where the foam is moving on a conveyor belt. Several study variables are taken, including the sample size, the sample location, the moisture percentage, the conveyor belt speed, and the microwave power. These experiments show that the sensor has a satisfactory accuracy in estimating the moisture distribution of the foam, and the ECT measurements can be further used in a closed-loop control system

A Combined Microwave Imaging Algorithm for Localization and Moisture Level Estimation in Multilayered Media

In this work, a multistatic uniform diffraction tomography (MUDT) method, that was proposed by the authors as a new qualitative imaging method just recently, is combined with the quantitative Bayesian inversion framework. In this combined approach, MUDT is applied to find the location of the moisture and this localization is employed as a pre-knowledge for the Bayesian framework to estimate the moisture levels in a polymer foam. The proposed combined algorithm might become a major part of the development of a new kind of intelligent industrial microwave drying systems. The imaging algorithm is tested with simulated measurement data. The frequency band from 8 GHz to 12 GHz (X-band) is used for the MUDT algorithm whereas a single frequency of 8.2 GHz is assumed for the Bayesian framework. The first results demonstrate the ability of the developed combined algorithm for optimizing the computational load unlike seen in the quantitative inversion approaches

An electromagnetic time-reversal imaging algorithm for moisture detection in polymer foam in an industrial microwave drying system

Microwave tomography (MWT) based control is a novel idea in industrial heating systems for drying polymer foam. In this work, an X-band MWT module is designed and developed using a fixed antenna array configuration and integrated with the HEPHAISTOS industrial heating system. A decomposition of the time-reversal operator (DORT) algorithm with a proper Green’s function of multilayered media is utilized to localize the moisture location. The derived Green’s function can be applied to the media with low or high contrast layers. It is shown that the time-reversal imaging (TRI) with the proposed Green’s function can be applied to the multilayered media with a moderately rough surface. Moreover, a single frequency TRI is proposed to decrease the measurement time. Numerical results for different moisture scenarios are presented to demonstrate the efficacy of the proposed method. The developed method is then tested on the experimental data for different moisture scenarios from our developed MWT experimental prototype. Image reconstruction results show promising capabilities of the TRI algorithm in estimating the moisture location in the polymer foam

Tomography-assisted control for the microwave drying process of polymer foams

This paper presents the integration of electrical capacitance tomography (ECT) with a moisture controller for the microwave drying of polymer foam. The proportional–integral (PI) control and the linear quadratic Gaussian (LQG) control are employed in designing the controller. The control objective in this process is that the moisture of polymer foam after the drying process reaches the desired set point. The permittivity distribution of polymer foam after the drying process is estimated in real-time using a designed ECT sensor and transferred as feedback to the controller. Since the permittivity and the moisture are strongly correlated, the material moisture can be controlled by controlling the permittivity. A state-space model is derived for the microwave drying process based on a system identification approach using the experimental data from the process. The derived model is employed in designing the LQG controller and adjusting the parameters of the PI controller. The designed controllers are implemented on a testbed microwave oven, and the experimental results show that the designed controllers are able to follow the desired set point moisture. The performance of the system with both controllers is compared, and their advantages and disadvantages are discussed. Moreover, the benefits of having a moisture controller for the microwave drying process are shown in simulation studies compared to an uncontrolled system

Monitoring of water content in a porous reservoir by seismic data: A 3D simulation study

A potential framework to estimate the amount of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the water storage reservoir system is modeled as a coupled poroviscoelastic-viscoelastic medium, and the underlying wave propagation problem is solved using a three-dimensional discontinuous Galerkin method coupled with an Adams-Bashforth time stepping scheme. The wave problem solver is used to generate databases for the neural network-based machine learning model to estimate the water content. In the numerical examples, we investigate a deconvolution-based approach to normalize the effect from the source wavelet in addition to the network's tolerance for noise levels. We also apply the SHapley Additive exPlanations method to obtain greater insight into which part of the input data contributes the most to the water content estimation. The numerical results demonstrate the capacity of the fully connected neural network to estimate the amount of water stored in the porous storage reservoir