Christian Brandstätter

Carbon pools and flows during lab-scale degradation of old landfilled waste under different oxygen and water regimes.

Landfill aeration has been proven to accelerate the degradation of organic matter in landfills in comparison to anaerobic decomposition. The present study aims to evaluate pools of organic matter decomposing under aerobic and anaerobic conditions using landfill simulation reactors (LSR) filled with 40 year old waste from a former MSW landfill. The LSR were operated for 27 months, whereby the waste in one pair was kept under anaerobic conditions and the four other LSRs were aerated. Two of the aerated LSR were run with leachate recirculation and water addition and two without. The organic carbon in the solid waste was characterized at the beginning and at the end of the experiments and major carbon flows (e.g. TOC in leachate, gaseous CO2 and CH4) were monitored during operation. After the termination of the experiments, the waste from the anaerobic LSRs exhibited a long-term gas production potential of more than 20 NL kg(-1) dry waste, which corresponded to the mineralization of around 12% of the initial TOC (67 g kg(-1) dry waste). Compared to that, aeration led to threefold decrease in TOC (32-36% of the initial TOC were mineralized), without apparent differences in carbon discharge between the aerobic set ups with and without water addition. Based on the investigation of the carbon pools it could be demonstrated that a bit more than 10% of the initially present organic carbon was transformed into more recalcitrant forms, presumably due to the formation of humic substances. The source of anaerobic degradation could be identified mainly as cellulose which played a minor role during aerobic degradation in the experiment.

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed. This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables. The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills. Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.

Theoretical analysis of municipal solid waste treatment by leachate recirculation under anaerobic and aerobic conditions

Long-term emissions of Municipal Solid Waste (MSW) landfills are a burden for future generations because of the required long-term aftercare. To shorten aftercare, treatment methods have to be developed that reduce long-term emissions. A treatment method that reduces emissions at a lysimeter scale is re-circulation of leachate. However, its effectiveness at the field scale still needs to be demonstrated. Field scale design can be improved by theoretical understanding of the processes that control the effectiveness of leachate recirculation treatment. In this study, the simplest and most fundamental sets of processes are distilled that describe the emission data measured during aerobic and anaerobic leachate recirculation in lysimeters. A toolbox is used to select essential processes with objective performance criteria produced by Bayesian statistical analysis. The controlling processes indicate that treatment efficiency is mostly affected by how homogeneously important reactants are spread through the MSW during treatment. A more homogeneous spread of i.e. oxygen or methanogens increases the total amount of carbon degraded. Biodegradable carbon removal is highest under aerobic conditions, however, the hydrolysis rate constant is lower which indicates that hydrolysis is not enhanced intrinsically in aerobic conditions. Controlling processes also indicate that nitrogen removal via sequential nitrification and denitrification is plausible under aerobic conditions as long as sufficient biodegradable carbon is present in the MSW. Major removal pathways for carbon and nitrogen are indicated which are important for monitoring treatment effectiveness at a field scale. Optimization strategies for field scale application of treatments are discussed.

Decision-making in the cognitive architecture SiMA

In a cognitive architecture, decision-making is the task that processes information from sensor data and stored knowledge to get appropriate action plans and actuator commands. Its aim is to make a decision in a given situation based upon available options and current goals of the system. In this paper, the decision-making process of the cognitive architecture SiMA is presented. Its unique features are the comprehensive evaluation of options, an application of case-based reasoning, as well as the management of resources by a two-step decision-making process. The implementation is verified through an artificial world implementation of a use case.

Comparison of technical filter mechanisms and defense mechanisms of the human mind

Looking for new paradigms in artificial intelligence, we are investigating functionalities of the human thinking process to manipulate information and filter perceptions. In this paper we introduce defense mechanisms of the human mind to be applied in artificial intelligence. We compare functionalities of defense mechanisms of the human mind with nowadays used filter mechanisms in artificial intelligence and explain reasons why defense mechanisms of the human mind open a broad new spectrum of possibilities and opportunities for artificial intelligence. In particular are these the defense mechanisms repression, deferral, sublimation, projection, disavowal, isolation, separation, depreciation and idealization. These defense mechanisms were chosen and devised with a team of psychoanalysts. We compare state-of-the-art artificial intelligence with psychoanalytic notions in our ongoing ARS project and explain why psychoanalysis is important for future developments in artificial intelligence. Finally, we give examples of similar projects.