Scalable Green Synthesis and Full‐Scale Test of the Metal–Organic Framework CAU‐10‐H for Use in Adsorption‐Driven Chillers

The demand for cooling devices has increased during the last years and this trend will continue. Adsorption‐driven chillers (ADCs) using water as the working fluid and low temperature waste energy for regeneration are an environmentally friendly alternative to currently employed cooling devices and can concurrently help to dramatically decrease energy consumption. Due to the ideal water sorption behavior and proven lifetime stability of [Al(OH)(m‐BDC)] ? x H₂O (m‐BDC^2? = 1,3‐benzenedicarboxylate), also denoted CAU‐10‐H, a green very robust synthesis process under reflux, with high yields up to 95% is developed and scaled up to kg‐scale. Shaping of the adsorbent is demonstrated, which is important for an application. Thus monoliths and coatings of CAU‐10‐H are produced using a water‐based binder. The composites are thoroughly characterized toward their mechanical stability and water sorption behavior. Finally a full‐scale heat exchanger is coated and tested under ADC working conditions. Fast adsorption dynamic leads to a high power output and a good power density. A low regeneration temperature of only 70 °C is demonstrated, allowing the use of low temperature sources like waste heat and solar thermal collectors.     

A Unifying View on SMT-Based Software Verification

After many years of successful development of new approaches for software verification, there is a need to consolidate the knowledge about the different abstract domains and algorithms. The goal of this paper is to provide a compact and accessible presentation of four SMT-based verification approaches in order to study them in theory and in practice. We present and compare the following different “schools of thought” of software verification: bounded model checking, k-induction, predicate abstraction, and lazy abstraction with interpolants. Those approaches are well-known and successful in software verification and have in common that they are based on SMT solving as the back-end technology. We reformulate all four approaches in the unifying theoretical framework of configurable program analysis and implement them in the verification framework CPAchecker. Based on this, we can present an evaluation that thoroughly compares the different approaches, where the core differences are expressed in configuration parameters and all other variables are kept constant (such as parser front end, SMT solver, used theory in SMT formulas). We evaluate the effectiveness and the efficiency of the approaches on a large set of verification tasks and discuss the conclusions.     

A sequential sampling strategy for adaptive classification of computationally expensive data

Many real-world problems in engineering can be represented and solved as a data-driven classification problem, where the goal is to build a classifier that maps a given set of input parameters onto a corresponding class or label. In some cases, the collection of data samples can be computationally expensive. It is therefore crucial to solve the problem using as little data as possible. To this end, a novel sequential sampling algorithm is proposed that begins with a very small training set and supplements it in each iteration by a small batch of additional (expensive) data points. The outcome is a representative set of data samples that focuses the sampling on those locations in the input space where the class labels are changing more rapidly, while making sure that no class regions are missed.     

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Hydropower is the most important source of electricity in Brazil. It is subject to the natural variability of water yield. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for short-term reservoir management, the use of probabilistic ensemble forecasts and multi-stage stochastic optimization techniques is receiving growing attention. The present work introduces a novel, mass conservative scenario tree reduction in combination with a detailed hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project Três Marias, which is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control downstream. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts are used to generate streamflow forecasts in a hydrological model over a period of 2 years. Results for a perfect forecast show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts of up to 15 days shows the practical benefit of operational forecasts, where stochastic optimization (15 days lead time) outperforms the deterministic version (10 days lead time) significantly. The range of the energy production rate between the different approaches is relatively small, between 78% and 80%, suggesting that the use of stochastic optimization combined with ensemble forecasts leads to a significantly higher level of flood protection without compromising the energy production.     

Short Term Optimal Operation of Water Supply Reservoir under Flood Control Stress using Model Predictive Control

Reservoir operations require enhanced operating procedures for water systems under stress attributed to growing water demand and consequences of changing hydro-climatic conditions. This study focuses on the management of the Yuvacik Dam Reservoir for water supply and flood mitigation in the Marmara Region of Turkey. We present an improved operating technique for fulfilling the conflicting water supply and flood mitigation objectives. This is accomplished by incorporating the long term water supply objectives into a Guide Curve (GC) whereas the extreme floods are attenuated by means of short-term optimization based on Model Predictive Control (MPC). The reference case implements operating rules with a constant GC at maximum forebay elevation targeting the fulfillment of the water supply objective. We compare the reference with a new time-dependent GC, derived using an Implicit Stochastic Optimization (ISO) approach. This new curve shows nearly the same performance regarding the water supply objectives, but significantly reduces the flooding risk downstream of the dam. Possible flood events observed at the end of the wet season, when the reservoir is at the maximum level to enable water supply for the dry season, can be eliminated by the application of an additional short-term optimization by MPC. The robustness of the approach is demonstrated via hindcasting experiments.     

User-adapted plan recognition and user-adapted shared control: A Bayesian approach to semi-autonomous wheelchair driving

Many elderly and physically impaired people experience difficulties when maneuvering a powered wheelchair. In order to ease maneuvering, powered wheelchairs have been equipped with sensors, additional computing power and intelligence by various research groups. This paper presents a Bayesian approach to maneuvering assistance for wheelchair driving, which can be adapted to a specific user. The proposed framework is able to model and estimate even complex user intents, i.e. wheelchair maneuvers that the driver has in mind. Furthermore, it explicitly takes the uncertainty on the user’s intent into account. Besides during intent estimation, user-specific properties and uncertainty on the user’s intent are incorporated when taking assistive actions, such that assistance is tailored to the user’s driving skills. This decision making is modeled as a greedy Partially Observable Markov Decision Process (POMDP). Benefits of this approach are shown using experimental results in simulation and on our wheelchair platform Sharioto.

Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data

This paper presents an ontology-driven approach for spatial database enrichment in support of map generalisation. Ontology-driven spatial database enrichment is a promising means to provide better transparency, flexibility and reusability in comparison to purely algorithmic approaches. Geographic concepts manifested in spatial patterns are formalised by means of ontologies that are used to trigger appropriate low level pattern recognition techniques. The paper focuses on inference in the presence of vagueness, which is common in definitions of spatial phenomena, and on the influence of the complexity of spatial measures on classification accuracy. The concept of the English terraced house serves as an example to demonstrate how geographic concepts can be modelled in an ontology for spatial database enrichment. Owing to their good integration into ontologies, and their ability to deal with vague definitions, supervised Bayesian inference is used for inferring complex concepts. The approach is validated in experiments using large vector datasets representing buildings of four different cities. We compare classification results obtained with the proposed approach to results produced by a more traditional ontology approach. The proposed approach performed considerably better in comparison to the traditional ontology approach. Besides clarifying the benefits of using ontologies in spatial database enrichment, our research demonstrates that Bayesian networks are a suitable method to integrate vague knowledge about conceptualisations in cartography and GIScience.     

Magnitude Vector Fitting to interval data

Vector Fitting is an effective technique for rational approximation of LTI systems. It has been extended to fit the magnitude of the transfer function in absence of phase data. In this paper, magnitude Vector Fitting is modified to work on inequalities which the magnitude of the transfer function has to satisfy, instead of least squares approximation. The new interval version of the magnitude Vector Fitting is proved valuable for multiband filter design and the fitting of noisy magnitude spectra.     

Algorithms for additive clustering of rectangular data tables

The overlapping additive clustering model or principal cluster model is a model for two-way two-mode object by variable data that implies an overlapping clustering of the objects and a set of profiles (characteristic variable values for each cluster). The model values of the variables of an object are the sum of the profiles of its corresponding clusters. In the associated data analysis the data matrix at hand is approximated by an overlapping additive clustering model of a prespecified rank by minimizing a least squares loss function. Recently an algorithm has been proposed for this purpose. This algorithm is a sequential fitting strategy, also called the method of principal clusters (PCL). Theoretical and empirical evidence that the PCL algorithm may have problems in revealing the true structure underlying a data set will be presented. As a way out, three new algorithms to fit the principal cluster model to empirical data will be presented: two of an alternating least squares (ALS) type, orthogonally combined with two different starting strategies, and one based on simulated annealing (SA). In a simulation study it is demonstrated that all three new algorithms outperform the existing PCL algorithm. The amount of objects that belong to more than one cluster (the overlap) is further found to have a considerable influence on the algorithmic performance of the ALS algorithms, with low amounts of overlap requiring a different starting strategy than high ones. As a consequence, for the analysis of real data sets in practice, a hybrid approach will be presented consisting of one of the ALS algorithms initialized by means of the two starting strategies under study.