This paper proposes a generalized active classification framework designed to classify the environment of a robot through controlled tactile interaction, with direct application to robotic disassembly of Waste Electrical and Electronic Equipment (WEEE). In this domain, purely vision-based systems frequently fail due to occlusions, dust, and reflective surfaces, making haptic identification an operational necessity. By formulating environment identification as an active sensing problem, we utilize the entropy of learned interaction models and a receding-horizon optimization to select control inputs that maximize information gain from force–torque feedback. Our approach transitions from a passive classifier to an active framework that dynamically probes the environment to resolve belief uncertainty across six distinct tactile classes, demonstrated through the high-precision detection and unfastening of screws using an industrial 6-DOF robotic arm. Experimental results on a consumer microwave demonstrate that the active framework achieves a 51% reduction in detection latency for screws compared to passive baselines and consistently outperforms expert-designed heuristics. Furthermore, the system exhibits robust zero-shot generalization to novel industrial objects, indicating that learned tactile signatures are invariant to the global object context. These results provide a scalable foundation for autonomous disassembly in high-variance environments like electronic waste recycling, while offering a broader methodology for any robotic classification task where control can be leveraged to accelerate information gain.
A sparse-Lagrangian particle implementation of the multiple mapping conditioning (MMC) model coupled to large eddy simulation (LES) for two-phase flows with reacting solid fuel particles is proposed. The MMC-LES model is developed and validated by means of carrier-phase direct numerical simulations (CP-DNS) of the devolatilization and volatile combustion from pulverized coal particles in statistically homogeneous isotropic turbulence (HIT). Two distinct Lagrangian particle clouds are introduced: The first cloud represents the inertial coal particles under going heat-up and pyrolysis, while the second cloud consists of stochastic MMC particles representing the reacting gas mixture of coal volatiles burning in air. To account for the heat and mass transfer between the two clouds, the one-to-one model for two-phase coupling in MMC is employed. To enhance model accuracy and practicality, a dynamic parameterization approach is proposed by deriving the governing MMC conditioning parameters, which are usually assumed to be constant, adaptively from the transient field of the volatile mixture fraction. Results show that conventional MMC predictions following best practice are in good agreement with the DNS for low fuel particle loadings, if suitable a-priori information (ideally from DNS) is available to calibrate the model. For high fuel particle loadings stronger deviations are observed. Moreover, we find that in conventional MMC the nominal conditioning parameters in physical and mixture fraction space r(m) and f(m) cannot be realized as physical and mixture fraction distances d(x) and d(f) for the most part of the transient ignition and volatile combustion process as expected. Differently, the new dynamic approach does not need a-priori calibration information from DNS, allows for a faithful match of the nominal and realized MMC conditioning parameters over time, and provides favorable predictions of the ignition and volatile combustion process. The sensitivity of the MMC results to the dynamic scaling factor alpha(f) and constant C-xi in the mixing time scale model is explored. A scaling factor alpha(f) = 0.03 is recommended, and while the standard value C-xi = 0.1 already yields reasonable predictions, improved results can be obtained for slightly smaller C-xi.
The storage and transport of renewable energy represent major challenges in the transition to a sustainable energy economy. Commonly proposed solutions involve chemical storage of hydrogen in molecules like ammonia, methanol, or liquid organic hydrogen carriers (LOHCs). In such cases, the efficient release of hydrogen from the carrier becomes a critical step. However, reforming ammonia into hydrogen and nitrogen under elevated pressures remains an underexplored topic. Catalytic performance and kinetic data at high pressures and temperatures are absent from the literature, primarily due to the technical challenges in obtaining reliable results and ensuring safe reactor operation. To overcome this gap, a double-walled reactor concept has been developed and constructed for high-pressure ammonia reforming, capable of operating at up to 50 bar and 750 degrees C. The reactor features axially resolved temperature monitoring and preventive measures have been implemented to avoid unintended catalytic activity on reactor walls, thermocouple surfaces, and dilution materials, ensuring reliable performance data. Reactor safety is maintained by preventing ammonia nitriding of sensitive reactor components. Prior to catalytic measurements, the technical features of the reactor have been systematically validated. A Ni/Al2O3 catalyst was tested at maximum operating conditions, achieving near-full NH3 conversion for GHSV values between 10 000 h-1 and 30 000 h-1. The highest recorded H2 productivity was 0.52 mmolH2/gcat/s. The mass transfer limitations were thoroughly investigated and excluded for the reported catalytic data. Finally, kinetic parameters for a modified power-law type kinetic model were determined for the first time at 50 bar and temperatures up to 750 degrees C, determining an apparent activation energy of 170.1 kJ mol-1, while the reaction orders for NH3 and H2 were found to be 0.76 and-1.22, respectively.
District-heating dispatch models are simplified, yet their decision costs are seldom quantified. This study distinguishes estimation bias, the objective-value difference between formulations, from decision regret, defined as an L1-relative forward-valued schedule gap based on each schedule’s cost when re-simulated with a high-fidelity forward model incorporating exponential heat losses, computed-friction hydraulics, and consumer differential-pressure constraints. Both are assessed through a 2×2 loss-by-topology set of decomposition controls, in which each contrast changes exactly one phenomenon, extended by a physics-evaluation framework running from a copperplate model to station-resolved hydraulics. Aggregate losses in the copperplate enable exact separation of loss and topology effects. Results are computed for one real network and 135 factorially designed synthetic networks. The copperplate underestimates operating costs by 15.1% but produces schedules whose unprovisioned loss, valued at the marginal unit, adds 46.1%; loss-aware schedules provision this loss and remain hydraulically deliverable. Loss visibility, not spatial topology, explains the discrepancy. Losses account for 95.8% of the copperplate-to-baseline gap, while topology alone, with physics fixed, adds 0.25%. Across synthetic networks, losses explain a median 100.0% of the gap. The topology effect remains within ±0.6% for trunk lengths of at least 5km and never exceeds 2.4%. Aggregate loss adders may conceal the deficiency in one network but do not transfer across scenarios. Forward evaluation of all 174 transmission stations and service laterals using real component data adds less than 1% to the fixed schedule and finds no hydraulic violation. Spatial routing is immaterial under central generation; distributed generation remains open. Results assume fixed capacities, a fixed heating curve, and radial networks.
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. Using Bayesian inference to solve real-world problems requires not only statistical skills, subject matter knowledge, and programming, but also awareness of the decisions made in the process of data analysis. All of these aspects can be understood as part of a tangled workflow of applied Bayesian statistics. Beyond inference, the workflow also includes iterative model building, model checking, validation and troubleshooting of computational problems, model understanding, and model comparison. We review all these aspects of workflow in the context of several examples, keeping in mind that in practice we will be fitting many models for any given problem, even if only a subset of them will ultimately be relevant for our conclusions.
