Decision support system of unplanned dilution and ore-loss in underground stoping operations using a neuro-fuzzy system

Unplanned dilution and ore-loss are the most critical challenges in underground stoping operations. These problems are the main cause behind a mine closure and directly influencing the productivity of the underground stope mining and the profitability of the entire operation. Despite being aware of the significance of unplanned dilution and ore-loss, prediction of these phenomena is still unexplained as they occur through complex mechanisms and causative factors. Current management practices primarily rely on similar stope reconciliation data and the intuition of expert mining engineers. In this study, an innovative unplanned dilution and ore-loss (uneven break: UB) management system is established using a neuro-fuzzy system. The aim of the proposed decision support system is to overcome the UB phenomenon in underground stope blasting which provides quantitative prediction of unplanned dilution and ore-loss with practical recommendations simultaneously. To achieve the method proposed, an uneven break (UB) prediction system was developed by an artificial neural network (ANN) considering 1076 datasets covering 10 major UB causative factors collected from three underground stoping mines in Western Australia. In succession, the UB consultation system was established via a fuzzy expert system (FES) in reference to surveyed results of fifteen underground-mining experts. The UB prediction and consultation system were combined as one concurrent neuro-fuzzy system that is named the ‘uneven break optimiser’. Because the current UB prediction systems in investigated mines were highly unsatisfactory with correlation coefficient (R) of 0.088 and limited to only unplanned dilution, the performance of the proposed UB prediction system (R of 0.719) is a remarkable achievement. The uneven break optimiser can be directly employed to improve underground stoping production, and this tool will be beneficial not only for underground stope planning and design but also for production management.     

A data‐centric framework for debugging highly parallel applications

Contemporary parallel debuggers allow users to control more than one processing thread while supporting the same examination and visualisation operations of that of sequential debuggers. This approach restricts the use of parallel debuggers when it comes to large scale scientific applications run across hundreds of thousands compute cores. First, manually observing the runtime data to detect error becomes impractical because the data is too big. Second, performing expensive but useful debugging operations becomes infeasible as the computational codes become more complex, involving larger data structures, and as the machines become larger. This study explores the idea of a data‐centric debugging approach, which could be used to make parallel debuggers more powerful. It discusses the use of ad hoc debug‐time assertions that allow a user to reason about the state of a parallel computation. These assertions support the verification and validation of program state at runtime as a whole rather than focusing on that of only a single process state. Furthermore, the debugger's performance can be improved by exploiting the underlying parallel platform because the available compute cores can execute parallel debugging functions, while a program is idling at a breakpoint. We demonstrate the system with several case studies and evaluate the performance of the tool on a 20 000 cores Cray XE6. Copyright © 2013 John Wiley & Sons, Ltd.     

Mining urban data (part A)

Modern cities are flooded with data. New information sources like public transport and wearable devices provide opportunities for novel applications that will improve citizens׳ quality of life. From a data science perspective, data emerging from smart cities give rise to a lot of challenges that constitute a new inter-disciplinary field of research. This article introduces the first part of a special issue on the topic ‘Mining Urban Data’ published in the journal Information Systems.     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

Geothermal energy utilization trends from a technological paradigm perspective

The use of geothermal energy and its associated technologies has been increasing worldwide. However, there has been little paradigmatic research conducted in this area. This paper proposes a systematic methodology to research the development trends for the sustainable development of geothermal energy. A novel data analysis system was created to research the geothermal energy utilization trends, and a technological paradigm theory was adopted to explain the technological changes. A diffusion velocity model was used to simulate and forecast the geothermal power generation development in the diffusion phase. Simulation results showed that the development of installed capacity for geothermal generation had a strong inertia force along with the S-curve. Power generation from geothermal power sources reached a peak in 2008 and is estimated to be saturated by 2030. Geothermal energy technologies in hybrid power systems based on other renewable energy sources look to be more promising in the future.     

新型硫酸法C_4烷基化工艺的模拟

提出了一种新型硫酸法C4烷基化生产工艺,丁烯原料在气相状态下进入反应器,与液相异丁烷、浓硫酸混合后,部分丁烯溶于液相,在液相中进行反应;通过控制反应器的压力,部分液相吸收反应热而汽化,使反应温度基本稳定;经气液分离、酸烃分离及产品分馏后,气相丁烯、异丁烷、浓硫酸分别构成循环。采用Aspen Plus过程模拟软件对新工艺过程进行模拟计算的结果表明,在反应器进口压力0.2 MPa、压降4.5 k Pa的条件下,反应器进出口温度均在7.2℃左右,可通过调节反应器压力实现温度的控制;反应器进口液相中烷烯质量比为145∶1,可减少副反应的发生;与传统硫酸法C4烷基化工艺相比,新工艺耗电量可降低30%。     

An industrial case study on variability handling in large enterprise software systems

ContextEnterprise software systems (e.g., enterprise resource planning software) are often deployed in different contexts (e.g., different organizations or different business units or branches of one organization). However, even though organizations, business units or branches have the same or similar business goals, they may differ in how they achieve these goals. Thus, many enterprise software systems are subject to variability and adapted depending on the context in which they are used.ObjectiveOur goal is to provide a snapshot of variability in large scale enterprise software systems. We aim at understanding the types of variability that occur in large industrial enterprise software systems. Furthermore, we aim at identifying how variability is handled in such systems.MethodWe performed an exploratory case study in two large software organizations, involving two large enterprise software systems. Data were collected through interviews and document analysis. Data were analyzed following a grounded theory approach.ResultsWe identified seven types of variability (e.g., functionality, infrastructure) and eight mechanisms to handle variability (e.g., add-ons, code switches).ConclusionsWe provide generic types for classifying variability in enterprise software systems, and reusable mechanisms for handling such variability. Some variability types and handling mechanisms for enterprise software systems found in the real world extend existing concepts and theories. Others confirm findings from previous research literature on variability in software in general and are therefore not specific to enterprise software systems. Our findings also offer a theoretical foundation for describing variability handling in practice. Future work needs to provide more evaluations of the theoretical foundations, and refine variability handling mechanisms into more detailed practices.     

Feasibility and effort estimation models for medium and small size information mining projects

Information mining is a sub-discipline of Information Systems which provides the non-trivial knowledge needed for making decisions inside an organization. Although such projects have different features from Software Engineering ones, they share some of their problems. Among these problems two are highlighted: unmanaged risks and inaccurate estimations of necessary resources to complete the project. In this context, this paper presents two ad-hoc models to be applied in Small and Medium-sized Enterprises: one for assessing project feasibility and the other for estimating the resources and time required to carry out the project. Both models should be applied at the beginning of the project.     

Simplifying the powder metallurgy manufacturing process using soft computing tools

The tools of soft computing will aid the knowledge mining in predicting and classifying the properties of various parameters while designing the composite preforms in the manufacturing of Powder Metallurgy (P/M) Lab. In this paper, an integrated PRNET (PCA-Radial basis functional neural NET) model is proposed in different versions to select the relevant parameters for preparing composite preforms and to predict the deformation and strain hardening properties of Al–Fe composites. It reveals that the predictability of this model has been increased by 67.89% relatively from the conventional models. A new PR-filter is proposed by slightly modifying the conventional filters of RBFNN, which improves the power of PRNET even though raw data are highly non-linear, interrelated and noisy. Moreover, fixing the range of input parameters for classifying the properties of composite preforms can be automated by the Fuzzy logic. These types of models will avoid expensive experimentation and risky environment while preparing sintered composite preforms. Thus the manufacturing process of composites in P/M Lab will be simplified with minimum energy by the support of these soft-computing tools.