Plant mediated methane efflux from a boreal peatland complex

Purpose Aerenchymous plants are an important control for methane efflux from peatlands to the atmosphere, providing a bypass from the anoxic peat and avoiding oxidation in the oxic peat. We aimed to quantify the drivers of aerenchymous peatland species methane transport and the importance of this process for ecosystem-scale methane efflux. Methods We measured seasonal and interspecies variation in methane transport rate per gram of plant dry mass at a boreal fen and bog, which were upscaled to ecosystem-scale plant methane transport. Results Methane transport rate was better explained by plant species, leaf greenness and area than by environmental variables. Leaves appeared to transport methane even after senescence. Contrary to our expectations, both methane transport rate and the proportion of plant transport were lower in the fen (with greater sedge cover) than in the bog site. At the fen and bog, average methane transport rate was 0.7 and 1.8 mg g−1 d−1, and the proportion of seasonally variable plant transport was 7–41% and 6–90%, respectively. Species-specific differences in methane transport rate were observed at the ecosystem-scale: Scheuchzeria palustris, which accounted for 16% of the aerenchymous leaf area in the fen and displayed the greatest methane transport rate, was responsible for 45% of the ecosystem-scale plant transport. Conclusion Our study showed that plant species influence the magnitude of ecosystem-scale methane emissions through their properties of methane transport. The identification and quantification of these properties could be the pivotal next step in predicting plant methane transport in peatlands.

A simulation model for the need for intra-plant transport operation planning by AGV

Intra-plant transport has an important role in the systems of enterprise logistics. At present, automated transport systems (AGV) are used for its efficient operation, with minimal operator attendance. For the proper and reliable functioning of such a transport system, there is currently a wide range of methods, of which the method of computer simulation is increasingly dominant. In the application, however, it is necessary to take into account that the functioning of AGV systems is a very demanding process with high demands on the used simulation software. Within the article, it will be described the use of the method of additional programming, as an effective tool in the creation of a simulation model of the AGV system for the need of its planning. The model presents the possibilities of increasing of the output of the analyzed production process to more than 70%, and at the same time it indicates insufficient use of workplaces that in one case reaches the value about 10%. The application of this model pointed to the reduction of the number of workplaces by one and with this related increase of output of other workplace.

Calibrating a wetland methane emission model with hierarchical modeling and adaptive MCMC

Methane (CH4) emission estimation for natural wetlands is complex and the estimates contain large uncertainties. The models used for the task are typically heavily parametrized and the parameter values are not well known. In this study we perform a Bayesian model calibration for a new wetland CH4 model to improve quality of the predictions and to understand the limitations of such models. The detailed process model that we analyze contains descriptions for CH4 production from anaerobic respiration, CH4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive MCMC techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in Southern Finland. The model parameters are calibrated using six different modeled peat column depths, and the hierarchical modeling allows us to assess the effect of the parameters on an annual basis. The results of the calibration and their cross validation suggest that the early spring net primary production and soil temperatures could be used to predict the annual methane emissions. The modeled peat column depth has an effect on how much the plant transport pathway dominates the gas transport, and the optimization moved most of the gas transport from the diffusive pathway to plant transport. This is in line with other research, highlighting the usefulness of algorithmic calibration of biogeochemical models. Modeling only 70 cm of the peat column gives the best flux estimates at the flux measurement site, while the estimates are worse for a column deeper than one meter or shallower than 50 cm. The posterior parameter distributions depend on the modeled peat depth. At the process level, the flux measurement data is able to constrain CH4 production and gas transport processes, but for CH4 oxidation, which is an important constituent of the total CH4 emission, the determining parameter is not identifiable.

Supplying of Assembly Lines Using Train of Trucks

The typical supply system conceptions, i.e. the concepts “Just-in-time” (JIT) and “Just-in-sequence” (JIS) are very important factors with regard to a fluent operation of the assembly lines. Therefore the contemporary intra plant transport systems are being replaced by a new kind of the transportation technology, namely by means of the trains of trucks. The trains of trucks are used in two possible operational modes: either with a driver or without driver (fully automated). The trucks of the logistic trains are also cheaper and they are able to carry a larger volume and mass of the material at once. There are reduced in this way not only the investment costs, but also the operational expenses.