Spatial targeting evaluation of energy and environmental performance of waste-to-energy processing

Waste-to-energy supply chains are important potential contributors to minimising the environmental impacts of municipal solid waste by reducing the amounts of waste sent to landfill, as well as the fossil fuel consumption and environmental footprints. Accounting for the spatial and transport properties of the waste-to-energy supply chains is crucial for understanding the problem and improving the supply chain designs. The most significant challenge is the distributed nature of the waste generation and the household energy demands. The current work proposes concepts and a procedure for targeting the size of the municipal solid waste collection zone as the first step in the waste-to-energy supply chains synthesis. The formulated concepts and the provided case study reveal trends of reducing the net greenhouse gas savings and energy recovery by increasing the collection zone size. Population density has a positive correlation with the greenhouse gas saving and energy recovery performance. For smaller zone size the energy recovery from waste approaches and in some cases may surpass the energy spent on waste transportation. The energy recovery and greenhouse gas savings remain significant even for collection zones as large as 200 km². The obtained trends are discussed and key directions for future work are proposed.

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Fuzzy linear model for production optimization of mining systems with multiple entities

Planning and production optimization within multiple mines or several work sites (entities) mining systems by using fuzzy linear programming (LP) was studied.LP is the most commonly used operations research methods in mining engineering.After the introductory review of properties and limitations of applying LP,short reviews of the general settings of deterministic and fuzzy LP models are presented.With the purpose of comparative analysis,the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines.After the assessment,LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering.After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models,the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.     

Antioxidant effects of flavonoids ofAnthriscus sylvestris in lard

Ethanol/water (7∶3) extracts of the plant speciesAnthriscus sylvestris possess antioxidant activity. Separation and identification of antioxidant components by thin-layer and column chromatography and spectral analysis demonstrated that quercetin and apigenin appeared to be the main flavonoid species inAnthriscus sylvestris. Rutin was one of the major quercetin glycosides. Structures of the isolated compounds were determined by infrared and¹H nuclear magnetic resonance spectroscopies. Ethanolic extract (70%) ofA. sylvestris showed concentration-dependent, strong antioxidant activity as determined by the Schaal Oven test of lard at 60°C. Rancimat analysis at 100°C showed that the antioxidant activity of 70% ethanolic extract ofA. sylvestris was superior to quercetin, apigenin, or a tocopherol mixture.     

Super‐macroporous dextran cryogels via UV‐induced crosslinking: synthesis and characterization

Cryogenic treatment and UV irradiation were exploited for the preparation of super‐macroporous cryogels from non‐modified high‐molar‐mass dextran. The photo‐crosslinking process was initiated by (4‐benzoylbenzyl)trimethylammonium chloride and N,N′‐methylenebisacrylamide (BAAm) was used as a crosslinking agent. Gel fraction yield and degree of swelling of the dextran cryogels were determined gravimetrically. Cryogel morphology and mechanical properties were studied using environmental scanning electron microscopy and dynamic rheological measurements, respectively. The effects of dextran concentration in the initial polymer solution, polymer molar mass and BAAm content on the crosslinking efficacy, physico‐mechanical properties and morphology of the cryogels were evaluated. The dextran cryogels were assessed as carriers of the model water‐soluble drug metoprolol. © 2017 Society of Chemical Industry     

Study of Algorithmic and Architectural Characteristics of Gaussian Particle Filters

In this paper, we analyze algorithmic and architectural characteristics of a class of particle filters known as Gaussian Particle Filters (GPFs). GPFs approximate the posterior density of the unknowns with a Gaussian distribution which limits the scope of their applications in comparison with the universally applied sample-importance resampling filters (SIRFs) but allows for their implementation without the classical resampling procedure. Since there is no need for resampling, we propose a modified GPF algorithm that is suitable for parallel hardware realization. Based on the new algorithm, we propose an efficient parallel and pipelined architecture for GPF that is superior to similar architectures for SIRF in the sense that it requires no memories for storing particles and it has very low amount of data exchange through the communication network. We analyze the GPF on the bearings-only tracking problem and the results are compared with results obtained by SIRF in terms of computational complexity, potential throughput, and hardware energy. We consider implementation on FPGAs and we perform detailed comparison of the GPF and SIRF algorithms implemented in different ways on this platform. GPFs that are implemented in parallel pipelined fashion on FPGAs can support higher sampling rates than SIRFs and as such they might be a more suitable candidate for real-time applications.     

Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly

This paper presents a methodology for generating a fuzzy inference mechanism (FIM) for recognizing contact states within robotic part mating using active compliant motion. In the part mating process, significant uncertainties are inherently present. As a result it is pertinent that contact states recognition systems operating in such environment be able to make decisions on the contact state currently present in the process, based on data full of uncertainties and imprecision. In such conditions, implementation of fuzzy logic and interval inference brings significant robustness to the system. As a starting point for FIM generation, we use a quasi-static model of the mating force between objects. By applying Discrete Wavelet Transform to the signal generated using this model, we extract qualitative and representative features for classification into contact states. Thus, the obtained patterns are optimally classified using support vector machines (SVM). We exploit the equivalence of SVM and Takagi–Sugeno fuzzy rules based systems for generation of FIM for classification into contact states. In this way, crisp granulation of the feature space obtained using SVM is replaced by optimal fuzzy granulation and robustness of the recognition system is significantly increased. The information machine for contact states recognition that is designed using the given methodology simultaneously uses the advantages of creation of machine based on the process model and the advantages of application of FIM. Unlike the common methods, our approach for creating a knowledge base for the inference machine is neither heuristic, intuitive nor empirical. The proposed methodology was elaborated and experimentally tested using an example of a cylindrical peg in hole as a typical benchmark test.     

Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning

The democratization of robotics technology and the development of new actuators progressively bring robots closer to humans. The applications that can now be envisaged drastically contrast with the requirements of industrial robots. In standard manufacturing settings, the criterions used to assess performance are usually related to the robot’s accuracy, repeatability, speed or stiffness. Learning a control policy to actuate such robots is characterized by the search of a single solution for the task, with a representation of the policy consisting of moving the robot through a set of points to follow a trajectory. With new environments such as homes and offices populated with humans, the reproduction performance is portrayed differently. These robots are expected to acquire rich motor skills that can be generalized to new situations, while behaving safely in the vicinity of users. Skills acquisition can no longer be guided by a single form of learning, and must instead combine different approaches to continuously create, adapt and refine policies. The family of search strategies based on expectation-maximization (EM) looks particularly promising to cope with these new requirements. The exploration can be performed directly in the policy parameters space, by refining the policy together with exploration parameters represented in the form of covariances. With this formulation, RL can be extended to a multi-optima search problem in which several policy alternatives can be considered. We present here two applications exploiting EM-based exploration strategies, by considering parameterized policies based on dynamical systems, and by using Gaussian mixture models for the search of multiple policy alternatives.