Complex Process Models Research Articles

Methodology for solar and wind energy chemical storage facilities design under uncertainty: Methanol production from CO2 and hydrogen

Abstract Production facilities that store solar or wind energy in the form of chemicals present underused capacity. The problem needs to address uncertain and variable operating conditions and prices for complex process models. An MILP formulation is developed including surrogate models based on the detailed NLP steady state models. The model solves the tradeoffs between investment and production capacity. This approach is applied to the case of the production of methanol from CO2 and solar or wind based hydrogen. Two cases are evaluated, Spain and UK. For the Spanish case, if electricity can be sold and there is no area restrictions, the process produces an excess of electricity with the solar panels available during summer time. Otherwise, electricity is only produced when excess capacity is available. In the UK, only wind turbines are used and the excess of electricity is produced during winter time.

A Qualitative Exploration of Adaptation Discussions During EBP Implementation

Balancing fidelity and adaptation during implementation of evidence-based practices is a critical issue for healthcare organizations; however, the processes involved in adaptation are not well understood. We build on the EPIS framework and ICT model by providing in depth information about inner and outer contextual factors that drive adaptation, as well as the processes that support discussions of adaptation. We examine 121 meeting and process notes using a grounded theory approach. Five types of adaptation are discussed in these documents. Key inner contextual drivers of adaptation relate to organizational characteristics and user characteristics, while outer contextual drivers involve funding, contracting arrangements, and client characteristics. Four emergent themes describe processes that can encourage adaptation discussions during active implementation: (1) setting boundaries between the practice and adaptation; (2) addressing resistance; (3) normalizing adaptation; and (4) cultivating psychological safety. This study examines naturalistic discussions of adaptation to lay the foundation for a more complex process model of adaptation during EBP implementation.

The intrinsic rate constants in diffusion-influenced reactions.

Intrinsic rate constants play a dominant role in the theory of diffusion-influenced reactions, but usually as abstract quantities that are implicitly assumed to be known. However, recently it has become clear that modeling complex processes requires explicit knowledge of these intrinsic rates. In this paper we provide microscopic expressions for the intrinsic rate constants for association and dissociation processes of isotropically interacting particles and illustrate how these rates can be computed efficiently using rare event simulations techniques. In addition, we address the role of the orientational dynamics, for particles interacting via anisotropic potentials.

A Personal Data Framework for Distributed User Modelling in New Service Ecosystems

User models generated from personal data are becoming a powerful tool used by service providers to personalize their supply, or introduce new features by means of recommendations or prediction of user behavior, among other applications. However, traditional user modelling requires each service provider to acquire and manage their users' personal information and carry out complex modelling processes. In this paper, we present a comprehensive personal data framework that distributes the user modelling process so that the personal data scattered in heterogeneous sources is used to generate on-demand, dynamic user models that can be retrieved by service providers to meet their business needs. Our proposal has been validated in a financial services scenario.

Model-based Approach to Automated Calculation of Key Performance Indicators for Industrial Turbines


 
 
 In recent years, the service business of the global turbo- machinery industry has undergone important changes. Many of these changes have been motivated by an increased demand for dedicated and systematic approaches to process safety, reliability, asset integrity and the overall health of the system. This has strengthened the role of key performance indicators (KPIs) as a means of providing guidance for the system’s health state and improve risk management. In order to provide trustable and accurate calculations of these performance indicators in an automated fashion, we argue for a model-based solution that deals with the complexity of diverse configurations and interdependences between system components. This paper presents a solution for calculating KPIs by a semi-automated process based on post-data processing from the site and specific system models. The models consist of a combination of system descriptions in terms of ontologies and complex event processing models. By virtue of our models, state indicator rules for KPI calculations can be formulated at different levels, identifying performance gaps and indicating precisely where action should be taken by the service engineers. With the adopted solution, we discuss the practical implementation and present results of our success story at Siemens AG for the Industrial Gas Turbines.
 
 

HAZOP analysis‐based dynamic simulation and its application in chemical processes

AbstractHazard and operability analysis (HAZOP) is widely applied in petroleum industry as a scientific and effective safety analysis method, but it is traditionally qualitative in essence and hereby has such shortages as vague conclusions, susceptible to human factor, lack of comprehensive consideration, and so on. This paper proposed a dynamic simulation‐based quantitative HAZOP method that utilizes dynamic simulation to reason deviations to reduce uncertainty in manual analysis. It transforms deviations into quantitative dynamic tasks and uses these tasks to activate dynamic simulation for quantitative deviation reasoning work. The possible consequence and propagation time caused by deviation is probed by analyzing simulation results. In this process, some deviations that appeared seldom or did not at all before can also be simulated and analyzed with the aid of dynamic mechanism model. Case studies in extractive distillation and ammonia synthesis loop showed that this method can present propagation effect of key process parameters on safety state and is useful for iterative and automatic assessment work. It is also found that detailed operation and device data are necessary for initial complex process modeling. In addition, we should improve simulation platform to meet practical requirements when applying this method. © 2015 Curtin University of Technology and John Wiley & Sons, Ltd.

Modelling of biohydrogen generation in microbial electrolysis cells (MECs) using a committee of artificial neural networks (ANNs)

The enhancement of hydrogen yield in microbial electrolysis cells (MECs) requires a robust process model that accurately relates the effect of anodic physicochemical input variables to the process output. Artificial neural networks (ANNs) have been used for the modelling of complex and non-linear processes. This paper reports the modelling of biohydrogen yield in MECs by using a committee of five ANNs. A topology of 6–(6, 8, 11, 12, 14)–1 was adopted, corresponding to the number of neurons of inputs, hidden (varied) and output layers. The ANN inputs were substrate type, substrate concentration, pH, temperature, applied voltage and reactor configuration. Model development was carried out with 50 data points from 15 published studies. The coefficients of determination (R2) between the experimental and predicted hydrogen yields for the five models were as follows: 0.90, 0.81, 0.85, 0.70 and 0.80. Model validation on new MEC processes showed a strong correlation between the observed and predicted hydrogen yields. Sensitivity analysis revealed that the performance of MEC was highly affected by variations in the substrate type, followed by applied voltage, substrate concentration, pH, MEC configuration and temperature in decreasing order. This study showed that the committee model accurately modelled the non-linear relationship between the considered physicochemical parameters of MEC and hydrogen yield, and thus could be used to navigate the optimization window in MEC scale-up processes.

Rainfall Runoff Analysis using Artificial Neural Network

Background/Objective: The main objective of the present study is to conduct laboratory experiment for the generation of rainfall runoff data using rainfall simulator. For the validation this observed data, a model is establish for estimating observed runoff data using Artificial Neural Network (ANN) technique. Methods: A total 12 laboratory experiments were conducted using rainfall simulator to generate runoff hydrograph using various slope and rainfall intensity over the catchment. For the validation of observed runoff hydrograph data were simulate using ANN. The ANN model was developed using collected 1076 data point to compute runoff discharge. For developing ANN model, the available data were separated as 70% for training, 15% for testing and 15% for validation. Results: The predicted results using ANN model performed better estimation with observed values which is useful for water resources planning and management etc. For the testing of model performance Nash-Sutcliffe efficiency criteria were used which gives NSE greater than 95%. Conclusion: The comparison of observed and predicted runoff hydrograph reveals that the Artificial Neural Network (ANN) predicts the runoff data reasonably well in observed hydrograph. It is found that ANNs are promising tools not only in accurate modeling of complex processes but also in providing insight from the learned relationship, which would assist the modeler in understanding of the process under investigation as well as in evaluation of the model.

Generating Stochastic Ellipsoidal Forest and Wildland Fire Scar Scenarios for Strategic Forest Management Planning under Uncertainty

Explicit consideration of the possibility of wildfires enhances strategic planning for landscape management; however, incorporating the stochastic processes that govern fire occurrence and spread in optimization models that are used to inform landscape management presents new challenges. In this article, we describe a method of generating spatially explicit fire scenarios in the form of elliptical fire scars with associated probabilities. Computational experiments demonstrate that good scenarios can be achieved using currently available computer technologies. We also describe how more complex fire ignition and growth process models can be used to generate more realistic fire pattern scenarios and provide insight into the quality and effort required for various approximations. We conclude by discussing how our methodology could be used to perform some of the tasks for which fire managers use traditional burn probability models.

Enhancement of waste activated sludge reduction potential by amalgamated solar photo-Fenton treatment

The solar photo-Fenton process, which is one of the advanced oxidation processes, offers a promising technology for minimization of excess sludge. This study assessed the effect of solar photo-Fenton treatment of waste activated sludge (WAS). The operating parameters, viz. pH, ferrous iron concentration and hydrogen per oxide were optimized as 3, 40 mg/L and 4 g/L. SCOD and TCOD were 380 mg/L and 1,700 mg/L at 4-h contact time. The effects of the three critical factors, viz. pH, ferrous iron concentration and hydrogen peroxide for pretreatment of WAS were simulated and evaluated using response surface methodology. This methodology has shown to be a valuable tool to model complex process such as the light-enhanced photo-Fenton reaction and to achieve optimum experimental parameters at minimal cost. Solar photo-Fenton process achieved 4.62% COD solubilization at 4-h contact time.

PolyNet: A Polynomial-Based Learning Machine for Universal Approximation

Currently, there is a need in all disciplines for efficient and powerful machine learning (ML) algorithms for handling offline and real-time nonlinear data. Industrial applications abound from real-time control systems to modeling and simulation of complex systems and processes. Certain ML methods have become popular with researchers and engineers. Such techniques include fuzzy systems (FSs), artificial neural networks (ANNs), radial basis function (RBF) networks, and support vector regression (SVR) machines. Historically, polynomial-based learning machines (PLMs) based on the group method of data handling (GMDH) model have enjoyed usage similar to that of these other methods. However, unwieldy kernel functions in the form of large high-order polynomials, and relatively limited computer speed and capacity, have limited the use of PLMs to comparatively small problems with low dimensionality and simple functional relationships. Thus, true polynomial-based ML solutions have drifted out of vogue for at least two decades. This work attempts to reinvigorate the interest in PLMs by introducing a novel practical implementation called PolyNet. It will be shown that once certain algorithms are applied to the generation, training, and functional operation of PLMs, they can compete on par with or better than methods currently in use.

Simulation of Gradostat Using System Simulation Software Tools

System simulations are used for modeling simple and complex processes that can be expressed in terms of ordinary differential equations, differential algebraic equations and discrete event simulation. There are many software tools for system simulations and all of them represent the physical system in a graphical form using many components connected together. A gradostat is an array of interconnected chemostats (bioreactors) and it is very suitable for modeling and simulation by system simulation tools. In this paper we demonstrate how to model and run system simulations of 3-vessel gradostat.

Further understanding of complex information processing in verbal adolescents and adults with autism spectrum disorders.

More than 20 years ago, Minshew and colleagues proposed the Complex Information Processing model of autism in which the impairment is characterized as a generalized deficit involving multiple modalities and cognitive domains that depend on distributed cortical systems responsible for higher order abilities. Subsequent behavioral work revealed a related dissociation between concept formation and concept identification in autism suggesting the lack of an underlying organizational structure to manage increases in processing loads. The results of a recent study supported the impact of this relative weakness in conceptual reasoning on adaptive functioning in children and adults with autism. In this study, we provide further evidence of the difficulty relatively able older adolescents and adults with autism have with conceptual reasoning and provide evidence that this characterizes their difference from age- and ability-matched controls with typical development better than their differences in language. For verbal adults with autism, language may serve as a bootstrap or compensatory mechanism for learning but cannot overcome an inherent weakness in concept formation that makes information processing challenging as task demands increase.

A human breathing lung-on-a-chip.

Here we describe a microphysiological system that replicates the functional unit of the living human lung. This human "breathing lung-on-a-chip" microdevice provides unique capabilities to reconstitute three-dimensional microarchitecture, dynamic mechanical activity, and integrated physiological function of the alveolar-capillary interface. We demonstrate the potential of this microengineered biomimetic model for screening environmental particulates and modeling complex human disease processes.

Modeling of Processes of Information Security Violations of Critical Infrastructure

Discusses the principles of evaluation the effectiveness of the malefactor in the critical infrastructure. The operating complex process modeling of security breach is presented. Investigated uncertainty modeling process of malefactor and ways to overcome them. A mathematical model of aggregate effectiveness of the malefactor, which removes some limitations of existing probabilistic models of random phenomena in the field of information security is developed. The model is called stochastic super indicator and is intended for research of conflict situations in critical infrastructure.

Dairy herd mastitis and reproduction: Using simulation to aid interpretation of results from discrete time survival analysis

Probabilistic sensitivity analysis (PSA) is a simulation-based technique for evaluating the relative importance of different inputs to a complex process model. It is commonly employed in decision analysis and for evaluation of the potential impact of uncertainty in research findings on clinical practice, but has a wide variety of other possible applications. In this example, it was used to evaluate the association between herd-level udder health and reproductive performance in dairy herds.Although several recent studies have found relatively large associations between mastitis and fertility at the level of individual inseminations or lactations, the current study demonstrated that herd-level intramammary infection status is highly unlikely to have a clinically significant impact on the overall reproductive performance of a dairy herd under typical conditions. For example, a large increase in incidence rate of clinical mastitis (from 92 to 131 cases per 100 cows per year) would be expected to increase a herd's modified FERTEX score (a cost-based measure of overall reproductive performance) by just £4.501 per cow per year. The herd's background level of submission rate (proportion of eligible cows served every 21 days) and pregnancy risk (proportion of inseminations leading to a pregnancy) correlated strongly with overall reproductive performance and explained a large proportion of the between-herd variation in performance.PSA proved to be a highly useful technique to aid understanding of results from a complex statistical model, and has great potential for a wide variety of applications within the field of veterinary science.

Retrieving Leaf Area Index from Remotely Sensed Data Using Advanced Statistical Approaches

Mapping and monitoring leaf area index (LAI) is critical to model surface energy balance, evapotranspiration, and vegetation productivity. Remote sensing helps in rapid collection of LAI on individual fields over large areas, in a time and cost-effective manner using empirical regression between LAI and spectral vegetation indices (SVI). However, these relationships may be ineffective when sun-surface sensor geometry, background reflectance and atmosphereinduced variations on canopy reflectance are larger than variations in the canopy itself. This requires development of superior and region-specific LAI-SVI models. In recent years, statistical learning methods such as support vector machines (SVM) and relevant vector machines (RVM) have been successful over the ordinary least square (OLS) regression models for complex processes. The objective of this study is to develop and compare OLS, SVM, and RVMbased reflectance models to estimate LAI for major summer crops in the Texas High Plains. The LAI was measured in 47 randomly selected commercial fields in Moore and Ochiltree counties. Data collection was made to coincide with Landsat 5 satellite overpasses on the study area. Numerous derivations of SVIs were examined for estimating LAI using OLS, SVM, and RVM models. Analysis of the results indicated that the SVI-LAI models based on the ratio of TM bands 4 and 3, and normalized difference vegetation index (NDVI) are most sensitive to LAI. The R2 values for selected models varied from 0.79 to 0.96 with the SVM model producing the best results. However, accuracy of reported LAI models needs further evaluation that accounts for in-field spatial variability in the LAI for wider applicability.

Scour Prediction at Bridge Piers in Cohesive Bed Using Gene Expression Programming

Accurate and reliable estimation of the scour depth at a bridge pier is essential for the safe and economical design of the bridge foundation. The phenomenon of scour at the pier placed on sediments is extremely complex in nature. Only a limited number of studies have been reported on local scour around bridge piers in cohesive sediment mainly due to the fact that scour modeling in cohesive beds is relatively more complex than that in sandy beds. Recent research has made good progress in the development of data-driven technique based on artificial intelligence (AI). It has been reported that AI-based inductive modeling techniques are frequently used to model complex process due to their powerful and non-linear model structures and their increased capabilities to capture the cause and effect relationship of such complex processes. Gene Expression Programming (GEP) is one of the AI techniques that have emerged as a powerful tool in modeling complex phenomenon into simpler chromosomal architecture. This technique has been proved to be more accurate and much simpler than other AI tools. In the present study, an attempt has been made to implement GEP for the development of scour depth prediction model at bridge piers in cohesive sediments using laboratory data available in literature. The present study reveals that the performance of GEP is better than nonlinear regression model for the prediction of scour depth at piers in cohesive beds.

The use of artificial neural network for the prediction of wear loss of aluminium-magnesium alloys

This paper reports on the effectiveness of a back–propagation artificial neural network model that predicts the wear loss of Al–Mg alloys samples. Artificial neural networks (ANNs) have the capacity to eliminate the need for expensive and difficult experimental investigation in testing and manufacturing processes. This paper shows that ANN can be employed for optimising the process parameters of aluminium alloys. The ANN predictions show very good agreement with experimental values with correlation coefficient of 0.823, thus ANN can be considered an excellent tool for modelling complex processes that have many variables.

A New Material Model for 2D FE Analysis of Adhesively Bonded Composite Joints

Effective and convenient stress analysis techniques play important roles in the analysis and design of adhesively bonded composite joints. A new material model is presented at the level of composite ply according to the orthotropic elastic mechanics theory and plane strain assumption. The model proposed has the potential to reserve nature properties of laminates with ply-to-ply modeling. The equivalent engineering constants in the model are obtained only by the material properties of unidirectional composites. Based on commercial FE software ABAQUS, a 2D FE model of a single-lap adhesively bonded joint was established conveniently by using the new model without complex modeling process and much professional knowledge. Stress distributions in adhesive were compared with the numerical results by Tsai and Morton and interlaminar stresses between adhesive and adherents were compared with the results from a detailed 3D FE analysis. Good agreements in both cases verify the validity of the proposed model. DOI: http://dx.doi.org/10.5755/j01.ms.20.4.5960