278 research outputs found

    A review of cleaner production in electroplating industries using electrodialysis

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    [EN] Cleaner production is an industrial preventive strategy created to promote benefits for the environment and for human beings. Its basic principle lies in using natural energy resources in an efficient way and yet in reducing risks and impacts on the environment and on human beings throughout the life cycle of a product. Electrodialysis is a membrane separation process which uses an electrical potential difference as a driving force to promote ionic separation in aqueous solutions. The technique was initially developed for the production of drinking water from brackish water. However, the use of electrodialysis in the treatment of industrial wastewaters is becoming more attractive, due to its characteristics. The technique is considered a clean process, since it allows the reuse of water and the recovery of substances. In this work, the advancement of electrodialysis applied to cleaner production in electroplating industry will be discussed. The aim of this work is to present electrodialysis as a technology which can fulfill the requirements of cleaner production concepts in the electroplating industry. The research was performed starting from a predefined question: "how is electrodialysis becoming a cleaner production strategy in the electroplating industry?". The research was divided in two main themes. The first search was related to the most important cleaner production practices applied for the plating industry. The second search was associated with the electrodialysis application in the electroplating industry. The results obtained from the collected publications were compared in order to propose an answer to the research question. The results showed that almost a half of the published articles evaluated the improvement of the wastewater treatment as a cleaner production strategy to be applied in plating industries. In addition, the wastewater treatment was the most cited application of electrodialysis in the plating industry, especially for copper, nickel and zinc recovery and for chromium VI removal. Results shows that electrodialysis is becoming an important and solid strategy to promote cleaner production in the plating industry. The two most important issues to be improved for this application are the system efficiency for macromolecules and the energy waste when dilute solutions are used. For the latter, the use of hybrid techniques such as electrodeionization was the most evaluated alternative. (C) 2017 Elsevier Ltd. All rights reserved.Authors would like to thank the Institute for Technological Research (IPT), the Institute for Technological Research Foundation (FIPT) and to The Sao Paulo Research Foundation (Fapesp - grants 2012/51871-9; 2014/13351-9 and 2014/21943-3).Scarazzato, T.; Panossian, Z.; TenĂłrio, J.; PĂ©rez-Herranz, V.; Espinosa, D. (2017). A review of cleaner production in electroplating industries using electrodialysis. Journal of Cleaner Production. 168:1590-1602. https://doi.org/10.1016/j.jclepro.2017.03.152S1590160216

    A Cleaner Production (CP) Perspective for the Metal Industry Processes: Case Study

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    Abstract: The study investigated the metal processing industry and established gaps in its application of cleaner production initiatives. Major processes were reviewed through use of material balance diagrams for typical operations. Upon which feasible CP options were generated to minimize waste and emissions from the metal industry. The specific framework guidelines for CP implementation were outlined for various metal industry sector processes

    Chemical Milling Increasing Efficiency at Wyman Gordon Company

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    Chemical Milling is a finishing process that removes oxide scale and alpha-case from forged titanium parts. To increase efficiency of chemical milling at Wyman-Gordon Company, we analyzed previous projects, assessed the etch quality of their chemical milling bath, and evaluated means of process control and potential areas of cost and raw material savings. Via our experimentation we determined that the ideal concentration of HF is 8% for all temperatures 90 degrees Fahrenheit to 120 degrees Fahrenheit to effectively etch a titanium part

    Hybrid Neural Network Controller Design for a Batch Reactor to Produce Methyl Methacrylate

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    Methyl methacrylate (MMA) production in an exothermic batch reactor provides a challenging problem for studying its dynamics behavior and temperature control. This work presents a neural network forward model (NN) to predict a concentration of methyl methacrylate, a jacket temperature and temperature profile in the reactor. An optimal NN model has been employed to predict state variables incorporating into a model predictive control (MPC) algorithm to determine optimal control actions. To control the temperature, neural network based control approaches: a neural network direct inverse control (NNDIC) and a neural network based model predictive control (NNMPC) have been formulated. In addition, a dynamic optimization approach has been applied to find out an optimal operating temperature to achieve maximizing the MMA product at specified final time. Simulation results have indicated that the NNMPC is robust and gives the best control results among the PID and NNDIC in all cases

    Neural Network Based Modeling and Control for a Batch Heating/Cooling Evaporative Crystallization Process

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    Crystallization processes have been widely used for separation in many fields to provide a high purity product. In this work, dynamic optimization and neural network (NN) have been applied to improve the quality of the product: citric acid. In the dynamic optimization, optimization problems maximizing both crystal yield and crystal size have been formulated. The neural networks have been developed to provide NN models to be used in the formulation of not only neural network inverse control (NNDIC) but also neural network model predictive control (NNMPC) strategies. The Levenberg Marquadt algorithm has been used to train the network and optimal neural network architectures have been determined by a mean squared error (MSE) minimization technique. In addition, a neural network model has been designed to provide estimates of the temperature and the concentration of the crystallizer. These estimates have been incorporated into the NNMPC controller. In the NNDIC controller, another neural network model has been applied to predict the set point of jacket temperature. The simulation results have shown that the obtained crystal size is increased by 19% and 30% compared to that by cooling and evaporation methods respectively and the obtained yield is increased more than 50%. The robustness of the proposed controller is investigated with respect to parameters mismatches. The results have shown that the NNMPC controller provides superior control performances in all case studies

    Instant Pickles: Generating Object-Oriented Pickler Combinators for Fast and Extensible Serialization

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    As more applications migrate to the cloud, and as “big data” edges into even more production environments, the performance and simplicity of exchanging data between compute nodes/devices is increasing in importance. An issue central to distributed programming, yet often under-considered, is serialization or pickling, i.e., persisting runtime objects by converting them into a binary or text representation. Pickler combinators are a popular approach from functional programming; their composability alleviates some of the tedium of writing pickling code by hand, but they don’t translate well to object-oriented programming due to qualities like open class hierarchies and subtyping polymorphism. Furthermore, both functional pickler combinators and popular, Java-based serialization frameworks tend to be tied to a specific pickle format, leaving programmers with no choice of how their data is persisted. In this paper, we present object-oriented pickler combinators and a framework for generating them at compile-time, called scala/pickling, designed to be the default serialization mechanism of the Scala programming language. The static generation of OO picklers enables significant performance improvements, outperforming Java and Kryo in most of our benchmarks. In addition to high performance and the need for little to no boilerplate, our framework is extensible: using the type class pattern, users can provide both (1) custom, easily interchangeable pickle formats and (2) custom picklers, to override the default behavior of the pickling framework. In benchmarks, we compare scala/pickling with other popular industrial frameworks, and present results on time, memory usage, and size when pickling/unpickling a number of data types used in real-world, large-scale distributed applications and frameworks

    DNN-Based ADNMPC of an Industrial Pickling Cold-Rolled Titanium Process via Field Enhancement Heat Exchange

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    The dynamic neural network based adaptive direct nonlinear model predictive control is designed to control an industrial microwave heating pickling cold-rolled titanium process. The identifier of the direct adaptive nonlinear model identification and the controller of the adaptive nonlinear model predictive control are designed based on series-parallel dynamic neural network training by RLS algorithm with variable incremental factor, gain, and forgetting factor. These identifier and controller are used to constitute intelligent controller for adjusting the temperature of microwave heating acid. The correctness of the controller structure, the convergence, and feasibility of the control algorithms is tested by system simulation. For a given point tracking, model mismatch simulation results show that the controller can be implemented on the system to track and overcome the mismatch system model. The control model can be achieved to track on pickling solution concentration and temperature of a given reference and overcome the disturbance

    A Multi-Code Analysis Toolkit for Astrophysical Simulation Data

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    The analysis of complex multiphysics astrophysical simulations presents a unique and rapidly growing set of challenges: reproducibility, parallelization, and vast increases in data size and complexity chief among them. In order to meet these challenges, and in order to open up new avenues for collaboration between users of multiple simulation platforms, we present yt (available at http://yt.enzotools.org/), an open source, community-developed astrophysical analysis and visualization toolkit. Analysis and visualization with yt are oriented around physically relevant quantities rather than quantities native to astrophysical simulation codes. While originally designed for handling Enzo's structure adaptive mesh refinement (AMR) data, yt has been extended to work with several different simulation methods and simulation codes including Orion, RAMSES, and FLASH. We report on its methods for reading, handling, and visualizing data, including projections, multivariate volume rendering, multi-dimensional histograms, halo finding, light cone generation and topologically-connected isocontour identification. Furthermore, we discuss the underlying algorithms yt uses for processing and visualizing data, and its mechanisms for parallelization of analysis tasks.Comment: 18 pages, 6 figures, emulateapj format. Resubmitted to Astrophysical Journal Supplement Series with revisions from referee. yt can be found at http://yt.enzotools.org
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