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Alarm floods are considered to be the major obstacles that prevent the smooth process operations of large-scale industrial facilities. During an alarm flood situation, industrial operators often get confused by too many alarms and thus have difficulties in observing and handling critical alarms. In recent years, sequence alignment based similarity analysis has emerged as an effective way to handle alarm floods. Alarm floods caused by the same fault are very likely to consist of the same group of alarms in a certain sequential order. Conducting realtime sequence alignment of industrial alarm floods can help operators to quickly recall the root cause and make prompt corrective actions. This paper proposes the online similarity analysis of alarm floods based on the Smith-Waterman and Needleman-Wunsch algorithms, and compares their differences and application conditions. Case studies are provided to illustrate the proposed online similarity analysis methods and the differences of the two sequence alignment algorithms.

Automatic control system is very common in modern industrial plants. The performance of the control loop largely depends on the control valve. Stiction is a commonly found problem in control valve. It produces oscillation in process output and decreases the longevity of the control valve. Oscillatory responses of the control loops due to valve stiction can disturb process operation, degrade product quality and decrease plant profitability. So, it is important to provide an efficient technique to minimize the detrimental effects of a sticky control valve before it can be sent for maintenance. In this research work, a stiction compensation technique of control valve is developed by adding an extra pulse with detuned controller action for a certain period of time. The performance of the proposed compensator is analyzed and compared with other stiction compensators available in the literature.

This paper presents the design and the online implementation of a monitoring station for a distillation column. The purpose of the monitoring station is to supervise and ensure the quality of the distilled product, through the analysis of the process operation, which can reduce production costs. The monitoring station is designed for a distillation column pilot plant in a batch-type operation mode, it is based on temperature measurements. It has 7 Pt-100 RTD temperature sensors, located in the boiler, the condenser and in 5 plates of the column body.

This paper investigates the optimal load-sharing problem of serial gas compressors in the presence of plant- model mismatch. The problem is formulated as a static real- time optimization task that is solved via modifier adaptation for interconnected systems. The proposed approach guarantees optimal operation of the plant upon convergence. Furthermore, it is shown how the specific problem structure can be exploited during process operation for the efficient estimation of plant gradients with respect to local inputs. A simulated case study demonstrates the effectiveness of the proposed real-time optimization approach.

In an ideal world, manufacturing devices would work all of the time, however, every company receives customer returns for a variety of reasons. If these returned parts contributed to a fail, most companies will perform failure analysis (FA) on the returned parts to determine the root cause of the failure. Failure can occur for a multitude of reasons, for example: wear out, fatigue, design issues, manufacturing flaw or defect. This information is then used to improve the overall quality of the product and prevent reoccurrence. If no defect is found, it is possible that in fact the product has no defect. On the other hand, the defect could be elusive and the FA techniques insufficient to detect said deficiency. No-clean flux residues can cause intermittent or elusive, hard to find defects. In an attempt to understand the effects of no-clean flux residues from the secondary soldering and cleaning processes, a matrix of varying process and cleaning operation was investigated. Of special interest, traveling flux residues and entrapped residues were examined, as well as localized and batch cleaning processes. Various techniques were employed to test the remaining residues in order to assess their propensity to cause a latent failure. These techniques include Surface Insulation Resistance1 (SIR) testing at 400C/90% RH, 5 VDC bias along with C32 testing and Ion Exchange Chromatography (IC). These techniques facilitate the assessment of the capillary effect the tight spacing these component structures have when flux residues are present. It is expected that dendritic shorting and measurable current leakage will occur, indicating a failing SIR test. However, since the residue resides under the discrete components, there will be no visual evidence of dendritic growth or metal migration.

In an industrial facility, a large amount of data on the operation of the facility is collected. The data includes control variables used in monitoring and regulation, alarm variables displayed to notify operators of abnormalities, and operators' actions taken to correct abnormalities as well as to operate the facility. As the data is historized and is readily accessible, it can provide a wealth of knowledge on the operation of the facility. Thus, this paper tackles the following question: Can the historized data be used to extract information to better understand process operation? More specifically, the paper attempts to answer the question by its two contributions, namely: (i) the development of performance metrics that characterize the time and frequency relations of events, and (ii) the development of two types of visualization plots to present the performance metrics in a succinct fashion. The paper also discusses the incorporation of the performance metrics and the visualization plots into industrial automation platforms.

Tools for predicting the plant process performances and forming the basis for controlling the process operation are essential for any wastwater treatment plant (WWTP). In this paper, extreme learning machine (ELM) with the ability of black-box modeling is considered. To further improve the performance of ELM, three intelligent algorithms are considered. In the proposed hybrid evolutionary ELM, intelligent algorithms (BDE, SaDE and TDE) are applied to optimize the network hidden neuron parameters and Moore-Penrose (MP) inverse is used to analytically determine the output weights. The proposed algorithms BDE-ELM SaDE-ELM and TDE-ELM are applied in the prediction of effluent from a biological WWTP. The simulation results show that hybrid evolutionary ELMs all performs better than basic ELM. And TDE is a superior method to improve the performance of ELM comparing with BDE and SaDE. Further more, TDE-ELM performs the best in the prediction accuracy among the three proposed methods.

A study of the optimal real-time management of hydrogen H 2 networks in an oil refinery has been carried out. The paper addresses the main problems related to the operation of the networks combining data reconciliation with a real-time optimization RTO system for the optimal production and redistribution of hydrogen. Coherent and robust results have been achieved regarding the data reconciliation stage despite all the uncertainty in measurements and process operation, according to the off-line validation performed. Once the network state has been estimated, a RTO approach was followed aiming at the real-time optimal operation of the global refinery H 2 network; an analysis of the solutions achieved was also performed.

The computer intelligent simulation technology will be used in the application of chemical experiment. The fields of chemical production operation process are integrated into the computer, so the system can save the production cost and avoid the unrepeatable of chemical process operation. We will talk about the concept of chemical experiment with the application of the model simulation and modern computer simulation methodology. It is important to the application of computer intelligent simulation technology in the chemical experiment and chemical practice.

Remanufacturing can effectively help reduce the environmental burden considering that it reduces both the natural resources needed and the waste produced. Currently, remanufacturing has been widely applied in the process instrumentation industry. Considering that the instrumentation makes up the brain of a process operation, how to remanufacture instrumentation plays a decisive role in the safety and economy of a plant. However, challenges such as what is the best time to performing remanufacturing and what components should be replaced, are still not being addressed because the remanufacturing decision-making method is missing. Therefore, a prognostics and health management (PHM)-based method is proposed for instrumentation system remanufacturing in this paper. First, different categories of components are identified for in-situ monitoring using failure modes, mechanisms, and effects analysis (FMMEA). Then, a system-level performance index (PI) for each component is calculated based on the PHM techniques. Finally, the component health index for each component is achieved based on the corresponding system-level PI by an application-specific mapping model. The remaining useful life of the instrumentation can be optimally used, and thus the cost of remanufacturing can be minimized by using the method proposed.

Alarm management has gained significance in the recent past mainly due to the drive to improve safety, reliability and efficiency of process operations. This paper highlights an in-house industrial implementation of a multitude of tools used for monitoring, analyzing and reporting alarm and safety system performance at LongLake site of Nexen Energy ULC. The efficacy of these tools, mainly used by plant engineers and control room operators in improving the alarm system performance is demonstrated through case studies. A brief overview of the implementation architecture is also presented in this paper.

Reliability, safety and efficiency of power plants become increasingly important due to the demand for cost-efficient energy production and tightening environmental regulations. Equipment malfunctions and faults are typical in industry but may lead to reduced production, shutdown of the plant or fatalities at the worst. Certain types of equipment faults induce exceptional behaviour that can be detected on the monitored variables and diagnosed before the severely damaging effects have occurred. Early intervention is often more cost-effective than allowing the equipment to fail. In this study, principal component analysis with different monitoring indices is used to monitor a desulphurization plant removing the sulphur dioxide from the flue gas of a CHP plant. Contributions of variables to the monitoring indices are checked during a special event. The approach is tested during normal process operation and during a period with a malfunctioning pump. The results show that the approach has potential for the early detection of an incipient fault.

Principal component analysis (PCA) method used in the fault diagnosis of industrial process is the most famous one of multivariate statistical methods. It concerns using few linear combinations of the set of process variables to explain the whole process operation state, and to detect the process fault. In this paper, the PCA method has been applied in Tennessee Eastman (TE) chemical process model. The simulation results show that PCA method can detect the fault quickly and effectively in some complex nonlinear chemical process.

The simulation of automated storage and retrieve system operation process is studied in detail by using flexsim software in this paper. Then the bottlenecks of process are analyzed in order to optimize the operation, eventually reducing system redundancy, improving equipment utilization, and speeding up the entire process operation rate are designed to achieve.

Financial problems are not only a significant research topic in the field of crisis management, but they also influence people's daily life and the operation of enterprises. To the great extent, finance crisis severely affects social stability and economic development and has a profound negative influence over the entire world. At present, China's banking industry has come to a critical juncture, how to control risks and grasp opportunities becomes the significant and urgent topic. Our banks also face an overwhelming amount of problems such as improper resource allocation, increasing bad loans, unreasonable operation and so forth. With the assistance of Process Operation Management theories and practices, this paper, from the perspective of crisis management and combined with the organizational operations, aims to analyze the banking problems of the operation situation. This paper will research the service blueprint concept and the service profit concept, and then make a range of suggestions on the effectiveness of banking organizational operations, which can be helpful to banking operation.

Statistical Process Control (SPC) is widely used for monitoring the performance of processes in manufacturing. Traditional SPC methods require trained individuals to read data which results in slow and limited detection. Much research has been devoted into developing an online automated system for SPC, so that the abnormality can be detected quickly and corrected by the process operation. To build a system as such, artificial neural networks (ANN) are widely used as tools where complex patterns can be difficult to recognize. Many research projects involve using random data patterns for training and recognition of patterns for ANN/SPC applications. However, many manufacturing processes involve autocorrelated data, to determine the effect of autocorrelated data, green sand data was analyzed and a neural network was built and trained to analyze a number of out of control patterns. Overall, the network performed best for detecting larger mean shifts.

The flotation column is a multivariable process whose main control objective is to guarantee the metallurgical yield set for the process operation, expressed by the grade and the recovery of the valuable mineral in the concentrate. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade as an index of control performance. Therefore, advanced new methods such as Artificial Neural Network (ANN) must be employed. In this work, it was used from Feed-Forward ANNs (FFANNs) method for the prediction of concentrate copper grade in flotation column using the real collected data, with 3–13–6–1 structure. The wash water and the non-floated flowrates and froth height were used as inputs to the network. The output of the model was percentage of Cu grade. It was achieved quite satisfactory correlations; so that R is equal 0.943 and 0.93 in training and testing stages for Cu grade prediction, respectively. The proposed NN model accurately estimates the effects of operational variables in column flotation plants and can be used in order to optimize the process parameters without having to conduct the new experiments in laboratory.

To deal with a kind of uncertainties existed in the complex industry processes, a new design method of robust proportional- integral- derivative (PID) controller based on coevolutionary particle swarm optimization was presented in the paper. The parameters tuning problem for robust PID controller was reduced to solve a maximum-minimum problem. A co- evolutionary algorithm based on particle swarm optimization (PSO) was used to solve this issue, and a PID controller with optimal robust performance index was obtained. For a continuous stirred tank reactor(CSTR) system under wide range of operating conditions, a robust PID controller was designed with the proposed method. The simulation results demonstrate the effectiveness of the proposed method, and showed that the PID controllers designed by the proposed method had better robustness and disturbance rejection in comparison with the one designed by LMI methods, and had a satisfying performance in a wide range of process operations.

Principal component analysis (PCA) has been widely studied and applied in continuous process monitoring and fault diagnosis. However, PCA can't be applied directly in batch processes due to the common multi-dimensionality of data matrix, uneven-length duration. Since the changes in the correlation may be used to indicate changes in the process operation stages, an optimal sub-stage PCA modeling method based on A-unfolding for uneven-length batch process is proposed, in which on the basis of analyzing the characteristics of sub-stage PCA modeling, the optimal model is established and the genetic algorithm is adopted to obtain the solution of optimal model. It is effective for batch processes with limited-runs modeling data and can improve the model precision. Simulation results to an injection molding process shows that the proposed method can partition the sub-stage accurately and it has better ability of process monitoring.

Current on-line wave-net learning algorithm adapts the primary identified process model with the new changes in time varying processes without a consideration of abnormal situations in the process operation. Therefore, if a disturbance occurs and makes changes in the process, current on-line learning updates the primary model to an unsuitable model. This paper proposes a procedure that first determines normal variations of time-varying processes from abnormal variations incorporating an adaptive dynamic principal component analysis (Adaptive DPCA) and updates the model only based on normal variations. A double continuously stirred tank reactors (CSTR) case study is invoked to show the effectiveness of the proposed approach. The results show the effectiveness of the method.

In this article, the successful application of INTEMOR (intelligent monitoring and management system for on-line real application) thermal power plant system is reported. By monitoring and analyzing the real-time and history data of industry, we can efficiently prevent incidents and manage process operation intelligently. It enables effective use of mathematical model and KT (knowledge technology). Both expertise advice (private knowledge) and relevant manual/regulations (common knowledge) are provided.

Reliability of the electrical power system is a critical factor for continuous process operations. This paper presents an analytical method for determining distribution system reliability that can be easily implemented to form a basis for plant improvement studies. The paper demonstrates the implementation of the failure modes and effects analysis (FMEA) and fault tree analysis (FTA) techniques based on the IEEE Gold Book. The analysis provides a basis, for informed decisions when selecting systems designed to increase system availability. The analytical techniques are illustrated using two simple distribution schemes and it is shown how the techniques may be applied to systems that are more complex.

Control of hot metal rolling process plays an important role in assuring high product quality and safe process operation. However, looperless interstand tension control remains a hard-to-solve problem due to strong interactions of adjacent rolling stands. This paper proposes a novel decoupling strategy to address this problem. Incorporating the proposed method with fuzzy control theory, an intelligent multiple stand tension control system is realized. Results from a virtual rolling test demonstrate the effectiveness and applicability of the proposed decoupling strategy.

In the steel rolling industry, control of the hot metal rolling process plays an important role in assuring high product quality and safe process operation. Although many advanced looper control technologies for finishing rolling process have emerged, looperless interstand tension control of roughing rolling mills remains a challenging problem. This paper proposes a multistand fuzzy tension control system for a roughing rolling mill. Combined with a novel decoupling strategy, the proposed scheme makes it possible to realize intelligent tension-free control of multiple roughing rolling stands. The results from a virtual rolling test demonstrated the applicability and effectiveness of the proposed technique.

This paper describes the results of a multi-fabricator site team in minimizing recipe diversity; the metrics used to measure the progress of the team are also defined. By identifying and measuring the differences in recipe and process locations within a route, process changes were implemented to make the process operations more standard between various fabricators and the technologies at a given fabricator. This work has resulted in eliminating unnecessary cleans, minimizing differences between various technologies, eliminating chemistries used for only a few recipes, and propagating the best practices among the fabricators.

Secondary plumes have become a major issue at cement plants, and regulatory agencies have increased their effort in enforcing opacity limits applicable to cement plants. This paper presents the results of trials at Inland Cement in Edmonton, Alberta, to abate the secondary plume, using innovative methods to remove reactants from the gas stream. These trials were completed with minimal capital and operating costs. The paper describes the chemical reaction, fate of ammonia and sulfur dioxide, and expected impact on process operations.

As an excellent open operation system, the UNIX system has been used widely as the development platform for application system development. Although UNIX has many characteristics that are suitable for supervisory control and data acquisition (SCADA) system, it really is a time-sharing operation system and has many obstacles for SCADA system development. This paper firstly introduces the character of UNIX that is suitable for SCADA system. In order to satisfy the real-time response requirement of a SCADA system, it discusses the reformation of the UNIX system from several aspects and the process operation, kernel parameter configuration and real-time operation improvement etc. The analysis tools for UNIX system performance are introduced and several solutions for poor system performances are discussed. After all the above improvement, the UNIX system is the best platform for SCADA system development.

Principal Component Analysis (PCA) has been successfully used to build a multivariate monitoring model for the process usually with one operation stage. However for processes with more than one operation stages, building a single PCA model to monitor the whole process operation performance may not be efficient and will lead to a high rate of missing alarms. To remedy this situation, a monitoring strategy using multiple PCA models is presented in this article based on soft-partition algorithms. The framework of utilizing multiple PCA models to monitor continuous processes is also introduced. The application to a three-tank plant demonstrates the effectiveness of the method.

Gray-box modeling attempts to combine both fundamental knowledge and empirical data to obtain a useful model of process dynamics. This idea may be implemented in various ways, and we have previously considered an implementation based on the idea of exact steady-state matching: the steady-state locus of the physical system to be modelled is assumed to be known, a model structure capable of matching that locus is chosen, and the steady-state of the model is forced to exactly match this locus. This constraint represents the fundamental knowledge incorporated in the model, and the remaining model parameters are then optimized to best fit available empirical data, obtained from typical (i.e., dynamic) process operation. This paper considers the question of what happens when our steady-state knowledge is not exact. In particular, we briefly consider the following topics: the extent of steady-state variability we might reasonably expect in practice, the consequences of that variability on our gray-box identification results, and some ideas for dealing with this variability based on the notions of set-theoretic parameter estimation.

Our focus is on change—the very rapid changes that are occurring which affect heat processing equipment used by electric motor repair facilities. • Energy costs are escalating and can be expected to keep right on increasing. Local shortages of certain fuels do occur and will continue to occur. Motor materia1s—core iron, core plate, and insulating varnishes—are changing, affecting both heat stripping and final varnish curing operations. • Heat process equipment is being modified or redesighed to meet changing material requirements and to be more energy effective. Operating principles and methods are changing as equipment changes.