Comparison between Artificial Neural Network and Rigorous Mathematical Model in Simulation of Industrial Heavy Naphtha Reforming Process

In this study, an artificial neural network (ANN) model was developed and compared with a rigorous mathematical model (RMM) to estimate the performance of an industrial heavy naphtha reforming process. The ANN model, represented by a multilayer feed forward neural network (MFFNN), had (36-10-10-10-34) topology, while the RMM involved solving 34 ordinary differential equations (ODEs) (32 mass balance, 1 heat balance and 1 momentum balance) to predict compositions, temperature, and pressure distributions within the reforming process. All computations and predictions were performed using MATLAB® software version 2015a. The ANN topology had minimum MSE when the number of hidden layers, number of neurons in the hidden layer, and the number of training epochs were 3, 10, and 100,000, respectively. Extensive error analysis between the experimental data and the predicted values were conducted using the following error functions: coefficient of determination (R2), mean absolute error (MAE), mean relative error (MRE), and mean square error (MSE). The results revealed that the ANN (R2 = 0.9403, MAE = 0.0062) simulated the industrial heavy naphtha reforming process slightly better than the rigorous mathematical model (R2 = 0.9318, MAE = 0.007). Moreover, the computational time was obviously reduced from 120 s for the RMM to 18.3 s for the ANN. However, one disadvantage of the ANN model is that it cannot be used to predict the process performance in the internal points of reactors, while the RMM predicted the internal temperatures, pressures and weight fractions very well.

Network topology optimization by turning non-scale-free networks into scale-free networks using nonlinear preferential rewiring method

In practical application, the generation and evolution of many real networks always do not follow rigorous mathematical model, making network topology optimization a great challenge in the field of complex networks. In this research, we optimize the topology of non-scale-free networks by turning it into scale-free networks using a nonlinear preferential rewiring method. For different kinds of original networks generated by Watts and Strogatz model, we systematically demonstrate the optimization process and the modified networks to verify the performance of nonlinear preferential rewiring. We conduct further researches to explore the effect of nonlinear preferential rewiring’s parameters on performance. Simulation results show that various non-scale-free networks with different network topologies generated by WS model, including random networks and various networks between regular and random, are turned into scale-free networks perfectly by nonlinear preferential rewiring method.

An Ontology for Reusable and Executable Decision Templates

Engineering design is increasingly recognized as a decision making process. Providing decision support is crucial to augment designers' decision-making capability in this process. In this paper, we present a template-based ontological method that integrates the decision-making mechanism with problem-specific information; thus, it can provide design decision support from both the “construct” and the “information” perspectives. The “construct,” namely, decision-making mechanism, is the utility-based Decision Support Problem (u-sDSP), which is a rigorous mathematical model that facilitates designers making multi-attribute selection decisions under uncertainty, while the information for decision making is archived as u-sDSP templates and represented using frame-based ontology to facilitate reuse, execution, and consistency-maintaining. This paper is an extension of our earlier work on the ontological modeling of the compromise decisions. The unique advantage of this ontology is that it captures both the declarative and procedural knowledge of selection decisions and represents them separately, thus facilitating designers reusing, executing previous documented decision knowledge to effect new decisions. The efficacy of ontology is demonstrated using a rapid prototyping (RP) resource selection example.

The Cognitive Mechanisms and Formal Models of Consciousness

Consciousness is the sense of self and the sign of life in natural intelligence. One of the profound myths in cognitive informatics, psychology, brain science, and computational intelligence is how consciousness is generated by physiological organs and neural networks in the bran. This paper presents a formal model and a cognitive process of consciousness in order to explain how abstract consciousness is generated and what its cognitive mechanisms are. The hierarchical levels of consciousness are explored from the facets of neurology, physiology, and computational intelligence. A rigorous mathematical model of consciousness is created that elaborates the nature of consciousness. The cognitive process of consciousness is formally described using denotational mathematics. It is recognized that consciousness is a set of real-time mental information about bodily and emotional status of an individual stored in the cerebellums known as the Conscious Status Memory (CSM) and is processed/interpreted by the thalamus. The abstract intelligence model of consciousness can be applied in cognitive informatics, cognitive computing, and computational intelligence toward the mimicry and simulation of human perception and awareness of the internal states, external environment, and their interactions in reflexive, perceptive, cognitive, and instructive intelligence.

Modelling ductile failure propagation during blowdown under localized jet fire attack

A rigorous mathematical model for determining the failure mode of pressurized vessels exposed to localized jet-fire impingement during rapid depressurization or blowdown is presented. Accounting for the thermodynamic trajectory of the two-phase hydrocarbon inventory, the method of separation of variables for non-homogenous heat conduction is used to determine the transient radial temperature gradient across the heated section of the vessel wall. The resulting temperature profile in conjunction with the appropriate vessel geometry stress equations are then used to simulate the transient triaxial thermal and pressure stress yield propagation. Failure is assumed to occur when any of the total stresses exceed the ultimate tensile strength of the vessel wall material. The application of the model to a real cylindrical vessel under localized jet fire attack in the vapour space reveals catastrophic failure involving rapid propagation of a tear along the major axis of the vessel wall due to severe thermal stress loading.