84 research outputs found
A multi-level approach for preliminary process development and a demonstration on developing whey-processing systems.
No full text
Otimização do planejamento da manutenção preventiva em sistemas complexos, com foco na cadeia de suprimento
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Produção.Sistemas de produção de grandes dimensões e complexos, compostos por múltiplos subsistemas de complexidade igualmente elevada, têm como uma de suas características a dificuldade em se determinar um plano ótimo de operações de longo prazo onde as intervenções para manutenção preventiva nos subsistemas sejam, do ponto de vista do resultado econômico geral, previstas para o momento realmente mais adequado. Neste trabalho é desenvolvida uma ferramenta de apoio às decisões relacionadas com a definição do planejamento das paradas de subsistemas produtivos para manutenção preventiva, considerando de forma central os aspectos relacionados com a produção e o mercado. A ferramenta desenvolvida emprega dois submodelos onde são solucionadas seqüencialmente etapas distintas do problema. Na primeira, utilizando-se de um modelo de programação linear, desenvolve-se uma base de dados formada a partir de soluções para o plano de produção em cada possível configuração de paradas nos subsistemas. Na segunda etapa, a partir da base de dados gerada inicialmente, o plano de paradas é obtido por meio da aplicação da técnica de algoritmo genético. Posteriormente, utilizando-se o estudo de caso de uma refinaria de petróleo, a metodologia desenvolvida é testada. Através de análises de sensibilidade com a alteração de parâmetros, a consistência das soluções obtidas é verificada e as conclusões e possibilidades de desenvolvimento são apresentadas
Predicting FTS products through artificial neural network modelling
No full textFischer-Tropsch synthesis is essential for converting CO2 into hydrocarbons, creating sustainable fuels and olefins. However, challenges in production yield and reaction kinetics remain. This study introduces an artificial neural network (ANN) to predict FT synthesis products from specific inputs, including temperature, pressure, GHSV, H2/CO2 ratio, and catalyst composition (Fe weight and K as a promoter). The ANN's ability to predict outputs like CH4, C2-4, C5+, CO2 conversion, and CO selectivity, without detailed reaction mechanisms, is a key innovation. This approach circumvents complex kinetic models. The network architecture is optimized for minimal error, and results are validated against a comprehensive database
Analyzing the effects of control Strategies for Determining Process Feasible Space
No full textThe identification of process Design Space (DS) is key to support the development of pharmaceutical processes, where strict requirements on manufacturability and product quality must be satisfied. If the process can be controlled by a set of manipulated variables, the DS can be enlarged with respect to an open-loop scenario, where there are no controls in place. Since pharmaceutical models are typically complex and computationally expensive, surrogate-based feasibility analysis can be suitably exploited to determine whether the process satisfies all constraints by adjusting the process control inputs, and mitigate the effect of uncertainty. The approach is successfully implemented on a pharmaceutical case study; results demonstrate that different control actions can be effectively exploited to mitigate uncertainty and operate the process in a wider range of inputs. The framework can conveniently be exploited to support decisions on control strategies for real industrial applications
Surrogate modeling application for process system emissions assessment: improving computational performances for plantwide estimations
No full textDuring the last decade, data driven modeling has gained a role of major interest all over the engineering fields mainly due to the need of higher computational power or, inversely, less computational demanding models for applications such as optimization, simulation, scheduling and control. Relevant contributions of process systems surrogate modeling as a support for operation optimization were already proved in literature with a reduction of the overall computational time by two orders of magnitude with respect to conventional simulations. In this research work a biogas-to-methanol plant case study is used to assess the total energy consumption and estimate the related emissions. Therefore, a modeling phase carried out via Response Surface Methodology is set up in order to obtain the analytical function that allows to estimate the equivalent CO2 emissions over an extended range of operating conditions representing a wide interval of biomass feed composition. The study has been performed over a wide independent variables domain as well as for different sample sizes in order to compare the computational performances and the accuracy of the obtained models accordingly. The computational time was reduced by two orders of magnitude with a mean relative error lower than 1%. Given the quality of the results, this approach could be further exploited for other system variables and processes including highly non-ideal behaviour of mixtures to be treated. Furthermore, more complex sampling and different surrogate modeling strategies could be tested in order to check if even higher computational effectiveness and model accuracy could be obtained in the process systems domain
A multiple model, state feedback strategy for robust control of non-linear processes
No full textThe major limitation of reported multiple model approaches is that robustness against process/controller disturbances cannot be addressed for processes consisting of hybrid stable/unstable regimes, or with chaotic dynamics. In this paper, a significantly modified multiple model approach is developed to achieve robust control with global stability. The new advances include: (1) stabilization of open-loop unstable plants using a state feedback strategy, (2) incorporation of an adjustable pre-filter to achieve offset-free control, (3) implementation of a Kalman filter for state estimation, and (4) connection of the multiple model approach with non-linear model predictive control to achieve a precise control objective. The improved controller design method is successfully applied to two non-linear processes with different chaotic behaviour. Compared with conventional methods without model modifications, the new approach has achieved significant improvement in control performance and robustness with a dramatically reduced number of local models
Solution techniques for transport problems involving steep concentration gradients: application to noncatalytic fluid solid reactions
No full textSome efficient solution techniques for solving models of noncatalytic gas-solid and fluid-solid reactions are presented. These models include those with non-constant diffusivities for which the formulation reduces to that of a convection-diffusion problem. A singular perturbation problem results for such models in the presence of a large Thiele modulus, for which the classical numerical methods can present difficulties. For the convection-diffusion like case, the time-dependent partial differential equations are transformed by a semi-discrete Petrov-Galerkin finite element method into a system of ordinary differential equations of the initial-value type that can be readily solved. In the presence of a constant diffusivity, in slab geometry the convection-like terms are absent, and the combination of a fitted mesh finite difference method with a predictor-corrector method is used to solve the problem. Both the methods are found to converge, and general reaction rate forms can be treated. These methods are simple and highly efficient for arbitrary particle geometry and parameters, including a large Thiele modulus. (C) 2001 Elsevier Science Ltd. All rights reserved
A formal representation of assumptions in process modelling
No full textIn this work, we present a systematic approach to the representation of modelling assumptions. Modelling assumptions form the fundamental basis for the mathematical description of a process system. These assumptions can be translated into either additional mathematical relationships or constraints between model variables, equations, balance volumes or parameters. In order to analyse the effect of modelling assumptions in a formal, rigorous way, a syntax of modelling assumptions has been defined. The smallest indivisible syntactical element, the so called assumption atom has been identified as a triplet. With this syntax a modelling assumption can be described as an elementary assumption, i.e. an assumption consisting of only an assumption atom or a composite assumption consisting of a conjunction of elementary assumptions. The above syntax of modelling assumptions enables us to represent modelling assumptions as transformations acting on the set of model equations. The notion of syntactical correctness and semantical consistency of sets of modelling assumptions is defined and necessary conditions for checking them are given. These transformations can be used in several ways and their implications can be analysed by formal methods. The modelling assumptions define model hierarchies. That is, a series of model families each belonging to a particular equivalence class. These model equivalence classes can be related to primal assumptions regarding the definition of mass, energy and momentum balance volumes and to secondary and tiertinary assumptions regarding the presence or absence and the form of mechanisms within the system. Within equivalence classes, there are many model members, these being related to algebraic model transformations for the particular model. We show how these model hierarchies are driven by the underlying assumption structure and indicate some implications on system dynamics and complexity issues. (C) 2001 Elsevier Science Ltd. All rights reserved
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