Optimal scheduling of single stage batch plants with direct heat integration

This paper addresses the multi-objective optimization problem arising in the operation of heat integrated batch plants, where makespan and utility consumption are the two conflicting objectives. A new continuous-time MILP formulation with general precedence variables is proposed to simultaneously handle decisions related to timing, product sequencing, heat exchanger matches (selected from a two-stage superstructure) and their heat loads. It features a complex set of timing constraints to synchronize heating and cooling tasks, derived from Generalized Disjunctive Programming. Through the solution of an industrial case study from a vegetable oil refinery, we show that major savings in utilities can be achieved while generating the set of Pareto optimal solutions through the ɛ-constraint method.     

初创型团队异质性与团队创新的关系:基于团队外部知识网络的视角

目的探索基于团队外部知识网络视角来考察年龄异质性和专业异质性对初创型异质性团队创新的作用。方法采用问卷调查的方法。对102个初创型团队进行调查。结果在初创型团队中,年龄异质性和专业异质性均能提升团队创新,外部知识网络利用强度和广度在年龄异质性促进团队创新的过程中承担中介作用,外部知识网络利用强度在专业异质性促进团队创新的过程中承担中介作用。结论在初创型团队中,年龄异质性通过提升外部知识网络利用促进团队创新,专业异质性通过提升外部知识网络利用强度促进团队创新。     

Application of integrated soft computing techniques for optimisation of hybrid CO2 laser–MIG welding process

In this paper, artificial neural networks (ANNs), genetic algorithm (GA), simulated annealing (SA) and Quasi Newton line search techniques have been combined to develop three integrated soft computing based models such as ANN–GA, ANN–SA and ANN–Quasi Newton for prediction modelling and optimisation of welding strength for hybrid CO₂ laser–MIG welded joints of aluminium alloy. Experimental dataset employed for the purpose has been generated through full factorial experimental design. Laser power, welding speeds and wires feed rate are considered as controllable input parameters. These soft computing models employ a trained ANN for calculation of objective function value and thereby eliminate the need of closed form objective function. Among 11 tested networks, the ANN with best prediction performance produces maximum percentage error of only 3.21%. During optimisation ANN–GA is found to show best performance with absolute percentage error of only 0.09% during experimental validation. Low value of percentage error indicates efficacy of models. Welding speed has been found as most influencing factor for welding strength.     

Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

Model predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems. To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. The novelty of our methodology lies in the application of POD's highly efficient linear decomposition for the consequent conversion of any distributed multi-dimensional space-state model to a reduced 1-dimensional model, dependent only on time, which can be handled effectively as a black-box through ANNs. Hence we construct a paradigm, which allows the application of NMPC to complex nonlinear high-dimensional systems, even input/output systems, handled by black-box solvers, with significant computational efficiency. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The stabilization/destabilization of a tubular reactor with recycle is used as an illustrative example to demonstrate the efficiency of our methodology. Case studies with inequality constraints are also presented.     

Local weather prediction system for a heating plant using cognitive approaches

Present-day requirements emphasize the need of saving energy. It relates mainly to industrial companies, where the minimization of energy consumption is one of their most important tasks they face. In our paper, we deal with the design of the so-called weather prediction system (WPS) for the needs of a heating plant. The primary task of such a WPS is timely predicting expected heat consumption to prepare the technology characterized by long delays in advance. Heat prediction depends primarily on weather so the crucial part of WPS is the weather, especially temperature, prediction. However, a prediction system needs a variety of further data, too. Therefore, WPS must be regarded as a complex system, including data collection, its processing, own prediction and eventual decision support. This paper gives the overview about existing data processing systems and prediction methods and then it describes a concrete design of a WPS with distributed data measuring points (stations), which are processed using a structure of neural networks based on multilayer perceptrons (MLP) with a combination of fuzzy logic. Based on real experiments we show that also such simple means as MLPs are able to solve complex problems. The paper contains a basic methodology for designing similar WPS, too.     

When sensing goes pervasive

In line with the pervasive vision, pervasive sensing allows the provision of ubiquitous and pervasive monitoring and heterogeneous data collection. In the past decade, two dominant pervasive sensing paradigms have emerged: a mostly human-free paradigm centered around wireless sensor networks and a human-centric paradigm fueled by the rise of personal smart devices (smartphones and wearables). In this paper, we review the key advances in these areas and outline our vision for future directions and developments.     

Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques

Metal-organic frameworks are a new class of materials for hydrogen adsorption/storage applications. The hydrogen storage capacity of this structure is typically related to pressure, temperature, surface area, and adsorption enthalpy. Literature provides no reliable correlation for estimating the hydrogen uptake capacity of MOFs from these easy-measured variables. Therefore, this study introduces several straightforward and accurate artificial intelligence (AI) techniques to fill this gap, initially determining the appropriate topology of AI-based methods, then comparing their performances by statistical criteria, and introducing the most accurate. This study used artificial neural networks, hybrid neuro-fuzzy systems, and support vector machines as estimators. The general regression neural networks (GRNN) with a spread of 7.92 × 10⁻⁴ shows the highest correlation with the literature data and provides a relative absolute deviation of 5.34%, mean squared error of 0.059, and coefficient of determination of 0.9946.     

Sequence networks to the calculation of two-simultaneous faults at the same location

The system of symmetrical components (012-system) can be used to simulate symmetrical and asymmetrical operation of power systems. However, the classical literature of Power Systems Analysis does not present one equivalent circuit in 012-system to two-simultaneous faults where there are a single-line-to-ground fault and a bolted line-to-line fault. This paper presents the equations and the new equivalent circuit mentioned. In addition, we present a comparison of results between the proposed approach and the output of the commercial software ANAFAS, which validates the methodology. We find evidence that the new equivalent circuit developed can substitute the equivalent circuits used to calculate a single line-to-ground fault and a bolted line-to-line fault.     

Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines

Model-based fault diagnosis tends to be too expensive or time-consuming to apply in the mineral processing industries, owing to the complexity and variability of operations. In contrast, data-based methods are inexpensive, but do not exploit the availability of first principle knowledge of plant operations. In this investigation, the use of process causality maps in conjunction with data-based fault diagnosis is considered as a hybrid methodology that can leverage the advantages of both approaches. Extreme learning machine algorithms are used to implement the data-based component of the approach. These algorithms can be deployed rapidly on large-scale systems and have the ability to deal with highly nonlinear systems. Two different variants are considered, viz. one used in combination with principal component analysis, as well as one with a bagging algorithm for fault diagnosis and applied to an industrial concentrator circuit in South Africa. The use of process causality maps led to significantly more effective fault diagnosis, while the use of extreme learning machines in combination with principal component analysis likewise allowed markedly better fault detection and diagnosis. In contrast, fault diagnosis with the bagging approach did not perform particularly well, owing to the high degree of correlation between the variables, which made it difficult to isolate individual causal variables.     

A neural network based technique for vibration characterization using Gaussian laser beams

This paper presents a neural network technique combined with an optical measurement system for the characterization of mechanical vibrations in industrial machinery. In the proposed system, the Gaussian beam of a laser source illuminates on an array of photodetectors. If either the laser source or the photodetector array is coupled with a vibrating system, then the optical powers detected by the photodetectors will vary accordingly, and are expected to reflect the magnitude and frequency of the X–Y planar vibrations of the monitored system. The time-varying optical powers are input to an artificial neural network-based vibration monitoring system which maps the power distributions to the X–Y position of the laser beam center. An experimental setup of the system is built and used for training and testing purposes. The obtained experimental results demonstrate the adequacy of combining optical techniques with neural networks to estimate the vibration frequency and magnitude. Estimated frequencies were within 1% of the actual ones, and the estimated magnitudes were within 29% of the actual magnitudes when using a chirp signal in the training phase. The magnitude estimation percentage error was further reduced below 12% when the neural network was trained with a decaying chirp signal.