Non-linear model parameter estimation–estimating a feasible parameter set with respect to model use

This article deals with non-linear model parameter estimation from experimental data. As for non-linear models a rigorous identifiability analysis is difficult to perform, parameter estimation is performed in such a way that uncertainty in the estimated parameter values is represented by the range of model use results when the model is used for a certain purpose. Using this approach, the article presents a simulation study where the objective is to discover whether the estimation of model parameters can be improved, so that a small enough range of model use results is obtained. The results of the study indicate that from plant measurements available for the estimation of model parameters, it is possible to extract data that are important for the estimation of model parameters relative to a certain model use. If these data are improved by a proper measurement campaign (e.g. proper choice of measured variables, better accuracy, higher measurement frequency) it is to be expected that a valid model for a certain model use will be obtained. The simulation study is performed for an activated sludge model from wastewater treatment, while the estimation of model parameters is done by Monte Carlo simulation.     

Decision Support for the Design of a New Production Plant Using Visual Interactive Simulation

Following promising results in test markets and successful pilot-scale manufacturing, the Board of a major Canadian manufacturer took the decision to proceed to develop the design for a full-scale production plant for a new advanced-technology product. The success of this new product was seen as critical to the Company's future, but Company personnel had limited experience with the new production technology and with the design of plants of this large scale. Finally, competitors from the USA and Europe were known to be working on similar products resulting in a sense of urgency in getting the product to the marketplace. A visual interactive decision support system built around a simulation model was developed using a methodology designed to help management understand the plant design issues and to help them resolve various key plant design options. When completed, the model gave management a tool which was used to analyse alternative plant designs and different production and manpower scheduling scenarios under a broad range of production conditions. The intensity of worldwide competition in this new product market, and the critical importance of this product to this Canadian corporation, have led to the decision to omit the name of the corporation from this article. In addition, certain production details have been disguised.     

Dynamic simulation of a tubular reactor for the production of low-density polyethylene using adaptive method of lines

The dynamic mathematical model of a tubular reactor for the production of low-density polyethylene (LDPE) is introduced and simulation studies of a LDPE plant are presented. The plant consists of the tubular reactor, compressors, heat exchangers and material recycles. The overall model formulation comprises differential, partial differential and algebraic equations. This model formulation is transformed into a DAE system using an adaptive method of lines approach, where the grid points may change their position but their number remains constant. With this technique a solution on a standard PC is possible.     

MODELING AND ANALYSIS OF THE ACID RAIN FORMATION DUE TO PRECIPITATION AND ITS EFFECT ON PLANT SPECIES

Abstract In this paper, a nonlinear mathematical model is proposed and analyzed to study the formation of acid rain in the atmosphere because of precipitation and its effect on plant species. It is considered that acid‐forming gases such as SO₂ , NO₂ emitted from various sources combine with water droplets (moisture) during precipitation and form acid rain affecting plant species. It is assumed that the biomass density of plant species follows a logistic model and its growth rate decreases with increase in the concentration of acid rain. The model is analyzed by using stability theory of differential equations and numerical simulation. The model analysis shows that as the concentration of acid rain increases because of increase in the cumulative emission rates of acid forming gases, the biomass density of plant species decreases. It is noted that if the amount of acid formed becomes very large, the plant species may become extinct.     

Mathematical modeling of a steam generator for sensor fault detection

A dynamic model for a nuclear power plant steam generator (vertical, preheated, U-tube recirculation-type) is formulated as a sixth-order nonlinear system. The model integrates nodal mass and energy balances for the primary water, the U-tube metal and the secondary water and steam. The downcomer flow is determined by a static balance of momentum. The mathematical system is solved using transient input data from the Philippsburg 2 (FRG) nuclear power plant. The results of the calculation are compared with actual measured values. The proposed model provides a low-cost tool for the automatic control and simulation of the steam generating process. The “parity-space” algorithm is used to demonstrate the applicability of the mathematical model for sensor fault detection and identification purposes. This technique provides a powerful means of generating temporal analytic redundancy between sensor signals. It demonstrates good detection rates of sensor errors using relatively few steps of scanning time and allows the reconfiguration of faulty signals.     

Derivation of a nine variable model of a U-tube steam generator coupled with a three-element controller

In this work a U-Tube Steam Generator – UTSG model derivation is presented. This model was developed as test bed to build a complete power plant simulator. The UTSG is an ordinary component of most of the Pressurized Water Reactor – PWR. A proportional-integral three-element controller was also coupled with the UTSG model. This model is part of a library called SIMODIS – SImulation MODeling Integrated System (“SIstema MODular Integrado de Simulação”). A few transient results are presented as well. The developed simulation program was implemented in MATLAB.     

Statistical identification and optimal control of thermal power plants

Statistical system identification and its use for the optimal control of thermal power plants are discussed. The analysis of the plant dynamics and derivation of the state-space representation are performed by fitting a multivariate AR model to the plant data obtained by an experiment. The basic concept of the power plant control and the motivation that necessitated the statistical approach are explained in the introduction. Practical procedure for the implementation of the method is described in detail with examples obtained from actual plants. The main items discussed are the selection of system variables by means of relative power contribution analysis, determination of the state equation and adjustment of the optimal feedback gain by digital simulation technique. Finally, excellent performance of the proposed control system is demonstrated by the operating records of 500 MW and 600 MW supercritical plants.     

Feedback methods for inverse simulation of dynamic models for engineering systems applications

Inverse simulation is a form of inverse modelling in which computer simulation methods are used to find the time histories of input variables that, for a given model, match a set of required output responses. Conventional inverse simulation methods for dynamic models are computationally intensive and can present difficulties for high-speed applications. This article includes a review of established methods of inverse simulation, giving some emphasis on iterative techniques that were first developed for aeronautical applications. It goes on to discuss the application of a different approach that is based on feedback principles. This feedback method is suitable for a wide range of linear and non-linear dynamic models and involves two distinct stages. The first stage involves design of a feedback loop around the given simulation model, and in the second stage, that closed-loop system is used for inversion of the model. Issues of robustness within closed-loop systems used in inverse simulation are not significant as there are no plant uncertainties or external disturbances. Thus, the process is simpler than that required for the development of a control system of equivalent complexity. Engineering applications of this feedback approach to inverse simulation are described through case studies that put particular emphasis on non-linear and multi-input multi-output models.     

Investment timing and optimal capacity choice for small hydropower projects

This paper presents a method for assessing small hydropower projects that are subject to uncertain electricity prices. We present a real options-based method with continuous scaling, and we find that there is a unique price limit for initiating the project. If the current electricity price is below this limit it is never optimal to invest, but above this limit investment is made according to the function for optimal size. The connection between the real option and the physical properties of a small hydropower plant is dealt with using a spreadsheet model that performs a technical simulation of the production in a plant, based on all the important choices for such a plant. The main results of the spreadsheet are simulated production size and the investment costs, which are in turn used for finding the value of the real option and the price limit. The method is illustrated on three different Norwegian small hydropower projects.     

Modeling plant disease with biological control of insect pests

Abstract

Introduction: This article discusses the problem of plant diseases that pose major threat to agriculture in several parts of the World. Herein, our focus is on viruses that are transmitted from one plant to another by insect vectors. We consider predators that prey on insect population leading to reduction in infection transmission of plant diseases. Methods: We formulate and analyze a deterministic model for plant disease by incorporating predators as biological control agents. Existence of equilibria and the stability of the model are discussed in-detail. Basic reproduction number R₀ of the proposed model is also computed and this helps in determining the impact of different key parameters on the transmission dynamics of disease. Additionally, the proposed model is extended to stochastic model and simulation results of both deterministic and stochastic models are compared and analyzed. Results: Our results of stochastic model show the less number of infected plants and insects compared to corresponding results for deterministic model. Also, our results analyze the impact of different key parameters on the equilibrium levels of infected plants and identify the key parameters. Discussion: Presented results are used to conclude and demonstrate that the biological control is effective in reducing the infection transmission of plant disease and there is a need to use plant-insect-specific predators to get desirable results.     

多种环境外力作用下作物生长系统的动力学模型及过程数值模拟

依据生物力学的理论与方法,综合探讨了多种环境因子(光照、温度、土壤水分与养分等)外力作用下作物生长的动力学机制.采用连续马尔柯夫过程方法建立了作物生长系统的动力学模型及数值模拟方法,研究了生长速率对外力条件变化的反应与生物量饱和值的变化,并以半干旱区作物为例进行了数值模拟计算与分析,为大田管理提供了定量依据.该动力模型与其它植物生长模型相比,其优越性在于从宏观和微观相结合上反映了资源利用的多维性,揭示了资源转化过程.所建作物生长系统模拟方法,具有先进性和可操作性,实际模拟结果与大田观测结果能很好吻合.     

Behavioural simulations in spot electricity markets

We study the consistency of behavioural simulation methods used to model the operations of wholesale electricity markets. We include different supply and demand representations and propose the Experience-Weighted Attractions method (Camerer and Ho, 1999) to encompass several behavioural paradigms. We compare the results across assumptions and to standard economic theory predictions. The match is good under flat and upward-slopping supply bidding, and also for plausible demand elasticity assumptions. Learning is influenced by the number of bids per plant and the initial conditions. The simulations perform best under reinforcement learning, less well under best-response and especially poorly under fictitious play. The overall conclusion is that simulation assumptions are far from innocuous. We link their performance to underlying features, and identify those that are better suited to model liberalised electricity markets.     

Forecast frequency in rolling horizon hedging heuristics for capacity expansion

This paper describes a simulation study of the effect of forecast revisions and hedges against demand uncertainty in a rolling horizon heuristic for capacity expansion. The model is based on data collected in the utilities division of a large chemical manufacturing plant. A seasonal integrated moving average model for the demand is used to generate forecasts, while capacity additions are determined by applying a simple timing rule to various hedges around the forecast. The simulation results indicate that hedging forecasts by their prediction limits rather than a fixed buffer significantly reduces undercapacity at the expense of a small increase in capacity cost. The prediction limit hedge is more robust to delays in reforecasting.     

Replenishment at Sea

This paper describes a case study on the logistics of replenishing a naval warship group. A discrete simulation model of the system was developed. A rule-based adviser was then embedded into the model, thus providing an intelligent decision-maker to automate the simulation model. The simulation methodology was later transformed from simple event-scheduling to the three-phase eLSE system, in preparation for future integration work. Some observations are made about the simulation methodologies used.     

Modelling and long-term simulation of a heat recovery steam generator

Developing accurate non-linear dynamical models for heat recovery steam generator (HRSG) units is presented in this article. The common non-linear autoregressive with exogenous input (NARX) system topology was employed to develop the neuro-fuzzy models based on the experimental data taken during field experiments. In this structure, the non-linear behaviours of the HRSG unit can be characterized through interpolation of local linear models associated with different operating regions via fuzzy inference mechanism. The operating regimes were recognized by applying a genetic algorithm-based fuzzy clustering technique to the prepared data sets. The structures of the fuzzy models are defined with respect to the obtained optimal cluster centres and the corresponding membership functions. The parameters of fuzzy rules were adjusted by recursive least-squares estimation method to fit the model responses to real data. The performances of developed models were evaluated by performing a comparison between the model responses and the responses of the real plant. In addition, the stability of the developed models was assessed by perturbing the model inputs from the nominal values. This guarantees the long-term simulation capabilities of the developed models. A comparison between the responses of the corresponding models and the models obtained from some recent modelling approaches was performed to show the advantages of the developed models. The results show the accuracy and reliability of the developed models at transient and steady-state conditions.     

A genetic algorithms simulation approach for the multi-attribute combinatorial dispatching decision problem

This paper presents a genetic algorithms (GA) simulation approach in solving a multi-attribute combinatorial dispatching (MACD) decision problem in a flow shop with multiple processors (FSMP) environment. The simulation is capable of modeling a non-linear and stochastic problem. GA are one of the commonly used metaheuristics and are a proven tool for solving complex optimization problems. The proposed GA simulation approach addresses a complex MACD problem. Its solution quality is illustrated by a case study from a multi-layer ceramic capacitor (MLCC) manufacturing plant. Because GA search results are often sensitive to the search parameters, this research optimized the GA parameters by using regression analysis. Empirical results showed that the GA simulation approach outperformed several commonly used dispatching rules. The improvements are ranging from 33% to 61%. On the other hand, the increased shop-floor-control complexity may hinder the implementation of the system. Finally, future research directions are discussed.     

A Metamodel Specification for a Tomato Processing Plant

This paper reports on the specification of a tomato processing plant cost-function using the Schruben-Cogliano (S-C) experimental procedure for response surface identification. The method is applied to a simulation model of a tomato processing plant in order to generate a cost-function metamodel that can be used to derive conditional factor-demand equations. The results suggest that the S-C methodology provides a means of deriving conditional factor-demand equations which reduces cost-function specification error.     

Modelling and simulation of mobile jet agitator for a biogas plant

This work deals with the modeling and simulation of mixture process in a biogas plant. This mixing process involves a closed pumping procedure, which pumps the constituents of the biogas plant using jet agitators inside the tank. For this purpose, an experimental model has been designed and built in reduced scale to study the mixture process. Furthermore, this process has been simulated numerically. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)