332,383 research outputs found

    Adaptive process control in rubber industry

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    This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive. The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good results even using only a few training samples

    neural network based modeling methodologies for energy transformation equipment in integrated steelworks processes

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    Abstract The paper proposes a methodology for modeling of energy transformation equipment which are commonly found in integrated steelworks, mainly focusing on steam production in the Basic Oxygen Furnace and auxiliary boilers, the electric power production in off-gas expansion turbines and some relevant steam and electricity consumers. The modeling approach is based on standard neural networks and Echo State Networks (ESN) for forecasting the variables of interest. All the models are intended as processes predictors to be used in a hierarchical control strategy based on multi-period and multi-objective optimization techniques and model predictive control. The overall target is the optimization of the re-use of off-gas produced in integrated steelworks by minimizing costs and maximizing revenues. Training and validation of models have been carried out by exploiting real historical data provided by steelmaking companies and have been successful tested

    Design principles for control of metabolism: Role of enzymatic regulation, redundancy and orthogonality

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    Improved understanding of the organization of metabolic networks can enable the more effective control of metabolism for several applications ranging from metabolite overproduction to treatment of metabolic diseases. Advances in computational modeling techniques have allowed the development of genome-scale models of metabolism in several organisms. These models have become the basis for analysing the potential of metabolic networks and to understand their organization. In this talk, we examine the design principles underlying the evolution of enzymatic regulation in metabolic networks using a model-based approach. We then evaluate the role of these regulatory networks in maintaining flux to a desired target metabolite. In the second part, we analyze the role of redundancy of metabolite production pathways and its implications for the robust production of the target metabolites. These observations shed light on the role of redundant modes of regulation and metabolic pathways for robust control of metabolic fluxes. Finally, we will discuss how orthogonality of production pathways can facilitate the effective control of fluxes through target metabolites and their implications for the evolution of modular pathways in metabolic network

    Thermodynamical Material Networks for Modeling, Planning and Control of Circular Material Flows

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    Waste production, carbon dioxide atmospheric accumulation and dependence on finite natural resources are expressions of the unsustainability of the current industrial networks that supply fuels, energy and manufacturing products. In particular, circular manufacturing supply chains and carbon control networks are urgently needed. To model and design these and, in general, any material networks, we propose to generalize the approach used for traditional networks such as water and thermal power systems using compartmental dynamical systems thermodynamics, graph theory and the force-voltage analogy. The generalized modeling methodology is explained, then challenges and future research directions are discussed. We hope this paper inspires to use dynamical systems and control, which are typically techniques used for industrial automation, for closing material flows, which is an issue of primary concern in industrial ecology and circular economy.Comment: Perspective paper in preparatio

    The Role of Eif6 in Skeletal Muscle Homeostasis Revealed by Endurance Training Co-expression Networks

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    Regular endurance training improves muscle oxidative capacity and reduces the risk of age-related disorders. Understanding the molecular networks underlying this phenomenon is crucial. Here, by exploiting the power of computational modeling, we show that endurance training induces profound changes in gene regulatory networks linking signaling and selective control of translation to energy metabolism and tissue remodeling. We discovered that knockdown of the mTOR-independent factor Eif6, which we predicted to be a key regulator of this process, affects mitochondrial respiration efficiency, ROS production, and exercise performance. Our work demonstrates the validity of a data-driven approach to understanding muscle homeostasis

    Robust Production Planning for District Heating Networks

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    Efficient use of energy becomes increasingly important in the modern society, with climate change as a driving factor. Short term production planning for district heating networks is motivated by a customer demand that varies according to a daily cycle, but which is directly dependent on changes in temperature and customer behaviour. The planning involves challenges in modeling of production unit startups and shutdowns, as well as modeling of heat storage. This thesis suggests the use of model predictive control, with a two stage stochastic programming problem solved at each iteration. Tests are performed for systems with and without accumulation. For both cases, the results indicate that the method has potential to generate savings

    Fault Tolerant Ancillary Function of Power Converters in Distributed Generation Power System within a Microgrid Structure

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    Distributed generation (DG) is deeply changing the existing distribution networks which become very sophisticated and complex incorporating both active and passive equipment. The simplification of their management can be obtained assuming a structure with small networks, namely, microgrids, reproducing, in a smaller scale, the structure of large networks including production, transmission, and distribution of the electrical energy. Power converters in distributed generation systems carry on some different ancillary functions as, for example, grid synchronization, islanding detection, fault ride through, and so on. In view of an optimal utilization of the generated electrical power, fault tolerant operation is to be considered as a suitable ancillary function for the next future. This paper presents a complete modeling of fault tolerant inverters able to simulate the main fault type occurrence and a control algorithm for fault tolerant converters suitable for microgrids. After the model description, formulated in terms of healthy device and leg binary variables, and the illustration of the fault tolerant control strategy, the paper shows how the control preserves power quality when the converter works in the linear range. The effectiveness of the proposed approach and control is shown through computer simulations and experimental results

    Proceedings of the third International Workshop of the IFIP WG5.7

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    Contents of the papers presented at the international workshop deal with the wide variety of new and computer-based techniques for production planning and control that has become available to the scientific and industrial world in the past few years: formal modeling techniques, artificial neural networks, autonomous agent theory, genetic algorithms, chaos theory, fuzzy logic, simulated annealing, tabu search, simulation and so on. The approach, while being scientifically rigorous, is focused on the applicability to industrial environment
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