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• First integrated optimization of crude oil procurement and blending. • Proposing a hybrid time-event representation to address different time horizons. • Considers the stability of blended crude oil properties in the optimization process. • Verification of the authenticity and validity of the model on the basis of real cases. • Optimization results in effective reduction of crude oil procurement costs. Crude oil procurement and blending are key processes in refinery production. When solving the integrated optimization problem of simple oil procurement and blending, the following issues are mainly considered: how to establish an integrated optimization model of crude oil procurement and blending process under different time scales to minimize the cost of procurement under the premise of ensuring the stability of the properties of blended crude oil. The paper proposes an integrated optimization model, takes cost minimization as the model's objective function, and provides stability through the yield and property constraints of blended crude oil. Then, the paper describes the procurement process by an event-based representation and the blending process by a continuous event representation and proposes a hybrid event-time-based representation to describe the model. Finally, this paper verifies the model's effectiveness in solving real production problems through case simulations. [ABSTRACT FROM AUTHOR]

Oscillations in control loops are one of the most prevalent problems in industrial processes. Due to their adverse effect on the overall process performance, finding how oscillations propagate through the process units is of major importance. This paper presents a method for integrating process causality and topology which ultimately enables to determine the propagation path of oscillations in control loops. The integration is performed using a dedicated search algorithm which validates the quantitative results of the data-driven causality using the qualitative information on plant connectivity. The outcome is an enhanced causal model which reveals the propagation path. The analysis is demonstrated on a case study of an industrial paperboard machine with multiple oscillations in its drying section due to valve stiction. [ABSTRACT FROM AUTHOR]

While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biologics, hindering the enormous potential for bioprocesses automation from their development to manufacturing. However, the adoption of ML-based models instead of conventional multivariate data analysis methods is significantly increasing due to the accumulation of large-scale production data. This trend is primarily driven by the real-time monitoring of process variables and quality attributes of biopharmaceutical products through the implementation of advanced process analytical technologies. Given the complexity and multidimensionality of a bioproduct design, bioprocess development, and product manufacturing data, ML-based approaches are increasingly being employed to achieve accurate, flexible, and high-performing predictive models to address the problems of analytics, monitoring, and control within the biopharma field. This paper aims to provide a comprehensive review of the current applications of ML solutions in the design, monitoring, control, and optimisation of upstream, downstream, and product formulation processes of monoclonal antibodies. Finally, this paper thoroughly discusses the main challenges related to the bioprocesses themselves, process data, and the use of machine learning models in monoclonal antibody process development and manufacturing. Moreover, it offers further insights into the adoption of innovative machine learning methods and novel trends in the development of new digital biopharma solutions. • Provide a review of applications of ML in antibody design and manufacturing. • Identifying challenges related to bioprocesses and process data. • Offering a valuable reference with a thorough discussion and public datasets. • Proposing machine learning (ML) research directions in antibody manufacturing. • Identifying advanced ML solutions for digital twins within biopharma 4.0. [ABSTRACT FROM AUTHOR]

• Large-scale combined scheduling and energy procurement optimization problems are solved. • Energy-cost optimization and energy-aware production scheduling are functionally separated. • Different variants of Mean Value Cross Decomposition are investigated that yield near-optimal or optimal solutions. • Existing solutions for the scheduling and for the energy procurement problem that take into account problem-specific aspects can be integrated. • Energy-intense industries as e.g. steel and pulp and paper can reduce their energy cost while meeting the production demands. Integrated optimization of the procurement cost of electric energy with production planning is increasingly considered in various industries. The traditional approach in industry is production driven, i.e. the production is scheduled first, followed by the energy supply optimization to find the best available energy portfolio, which is usually sub-optimal. The combined scheduling and energy procurement optimization can be formulated as an integrated monolithic optimization model, resulting in intractable problems, even if solutions to the two isolated problems are available. We propose to use Mean Value Cross Decomposition for solving the combined problem by iterating between energy-aware production scheduling and energy-cost optimization, possibly building on existing solutions. We apply the approach to a pulping process and a steel production process. MILP-based models are employed for the two scheduling problems and for the energy cost optimization a Minimum-Cost Flow Network model is used, resulting in good quality solutions within reasonable computation times. [ABSTRACT FROM AUTHOR]

• Developed extension of the acceptance-rejection method for identification of the design space (DS) in a two-stage batch reactor system. • Studied the time dynamics and the effect of critical quality attributes (CQAs) on critical process parameters (CPPs) between different stages. • Studied different settings of imposed CQAs and their connections with tight operations versus balanced operations. • Employing the potential second stage DS volume as a quality metric enables adjusting DS sizes and transition between tight and balanced operations. The design space (DS) is defined as the combination of materials and process conditions that guarantees the assurance of quality. This principle ensures that as long as a process operates within DS, it consistently produces a product that meets specifications. It was originally developed for a single unit system. Many industrial processes frequently involve multiple unit operations. Assessing the interaction of Critical Process Parameters (CPPs) with Critical Quality Attributes (CQAs) across stages enables informed decision-making and the capacity to balance different requirements. Analysis and visualization of the complex, multi-dimensional DS is a challenging task. This paper presents a framework for identification such DSs, considering both joint and decoupled strategies using a detailed analysis of a two-stage batch reactor case study. We assess and discuss the practicality and relevance of these methods. [ABSTRACT FROM AUTHOR]

• A data enhancement method based on generative adversarial network is proposed. • The method combines the advantages of data augmentation and feature selection. • Case studies on a simulated case and two real industrial processes are provided. • Comparisons to state-of-the-arts show effectiveness of the proposed method. Soft sensor plays an important role in improving product quality; however, practical applications may often face with the problem of small sample size, which is challenging for developing data-driven models in terms of feature selection and good generalization. This paper proposes a data enhancement approach for small sample size data-driven problems based on generative adversarial networks integrated with maximum relevance minimum redundancy (MRMR). First, sample expansion is performed on the initial data by using a generative adversarial network. Second, irrelevant variables are eliminated by the MRMR and optimal features are obtained. Finally, neural networks-based soft sensor modeling is performed using the augmented dataset and the selected features. The proposed method is tested on a simulated penicillin case, an actual penicillin production case and an actual erythromycin production case. Experimental results show that the proposed method outperforms state-of-the-art existing methods, which verify the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

• Pharmaceutical process design involves much uncertainty in the data on which it is based. • The paper presents a quantitative approach to risk management for design using a stochastic approach which identifies key data required and allows for its systematic incorporation. • Expected performance was improved by considering the propagation of uncertainty over the whole process. • In this case study it was found that below a certain value in the feed purity 'perfect' knowledge had a very significant impact. Process plants for the manufacture of pharmaceutical products often need to be designed and built quickly to make the most of available patent life which necessitates using uncertain or unavailable data. It is common that pilot plant equipment and data are available and new data can be generated if they are important. We present a model based approach to risk analysis to aid design for pharmaceutical processes which combines systematic modelling procedures with Hammersley sampling based uncertainty analysis and sensitivity analysis used to quantify predicted performance uncertainty and to identify key uncertainty contributions. The main contribution of the paper is the demonstration of the methodology on an industrial case study where the process flowsheet was fixed and some pilot data was available. Expected performance was improved by considering the propagation of uncertainty over the whole process. The case study results indicate the importance of considering uncertainty systematically and quantitatively. The methodology showed the opportunity to improve process performance potential through considering uncertainty systematically. [ABSTRACT FROM AUTHOR]

Big data and time series are typical features of modern industrial process data. Effective time series modeling methods are required for ensuring the normal and stable operation of industrial processes. However, the existing time series prediction methods usually cannot take important historical data features long ago into consideration. In this paper, we proposed a new attention-mechanism based dynamic-inner partial least squares long short-term memory (ADiPLS-LSTM) method to predict the time series data. First, the DiPLS method is applied to extract the dynamic features in the selected data group, and the attention-mechanism is used then to define the importance of the responded features. The product of the extracted dynamic features and the attention-mechanism results is regarded as the input of LSTM to predict the future time information. The proposed method uses both the recent data information and important data information in a long run, which helps to obtain more accurate prediction results. The effectiveness of the ADiPLS-LSTM method is verified by the data of a 660MW coal-fired boiler. Compared with BPNN, LSTM, and DiPLS-LSTM, the proposed method also advances in time series data prediction and interpretability. • The receive field of LSTM is usually very limited, and the interpretability is weak • DiPLS can extract dynamic features between input and output • The existing prediction methods cannot take important historical data features. • A new novel ADiPLS-LSTM method is proposed in this paper. • Application results confirm the effectiveness and reliability of the proposed method. [ABSTRACT FROM AUTHOR]

Bayesian optimization (BO) has emerged as a useful paradigm for automatic calibration (aka auto-tuning) of advanced optimization- and learning-based controllers whose closed-loop performance depends on the choice of several tuning parameters in highly nonlinear and nonconvex ways. However, BO approaches to controller auto-tuning commonly rely on the assumption that system dynamics remain constant, which does not hold for systems with time-varying dynamics, for example, due to gradual aging or persistent environmental drifts. This challenge can be further compounded when gradual and persistent system drifts occur over a series of process runs. Existing time-varying BO (TVBO) approaches with spatio-temporal kernels fall short of effectively handling an integer run index, which is imperative for capturing run-to-run changes in the system behavior. To this end, this paper presents a run-indexed TVBO (RI-TVBO) approach that can systematically account for run-to-run process drifts as the system is queried over sequential process runs. The proposed approach relies on adapting the non-stationary Wiener process kernel to accommodate an integer run index, instead of time. This is done via positional encoding that incorporates the integer run index and, thus, enables describing run-to-run variations in system dynamics. The positional embedding vector associated with each run index is then mapped onto a scalar value to leverage the relationships between different process runs within the probabilistic surrogate model of the objective function in RI-TVBO. The performance of RI-TVBO is evaluated for auto-tuning of an offset-free model predictive controller for a low-temperature plasma-assisted process for thin film deposition. Simulation results demonstrate the superior performance of RI-TVBO over standard BO and TVBO under different scenarios of run-to-run process drifts encountered in plasma-assisted deposition processes in semiconductor manufacturing. • A run-indexed time-varying Bayesian optimization method is presented that can systematically handle run-to-run system drifts and variations. • The performance of the proposed method is demonstrated for auto-tuning of an offset-free MPC controller. • Simulation results demonstrate the effectiveness of the proposed method in handling run-to-run process drifts that commonly occur in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

This paper presents a multi-week mixed integer linear programming (MILP) scheduling model for an ice cream processing facility. The ice cream processing is a typical complex food manufacturing process and a simplified version of this processing has been adapted to investigate scheduling problems in the literature. Most of these models only considered the production scheduling for a week. In this paper, multi-week production scheduling is considered. The problem has been implemented as an MILP model. The model has been tested on a set of cases from the literature, and its results were compared to the results of problems solved using hybrid MILP-heuristics methods in the literature. The inclusion of clean-up session, weekend break and semi-processed product from previous week were also assessed with two additional sets of experiments. The experiments result show that the proposed MILP is able to handle multi-week scheduling efficiently and effectively within a reasonable time limit. [ABSTRACT FROM AUTHOR]

To cope with a cost-effective manufacturing approach driven by more than Moore’s law era, plasma etching which is one of the major processes in semiconductor manufacturing has developed plasma sensors and their applications. Among the plasma sensors, optical emission spectroscopy (OES) has been widely utilized and its high dimensionality has required multivariate analysis (MVA) techniques such as principal component analysis (PCA). PCA, however, might devaluate physical meaning of target process during its statistical calculation. In addition, inherent noise from charge coupled devices (CCD) array in OES might deteriorate PCA model performance. Therefore, it is desirable to pre-select physically important variables and to filter out noisy signals before modeling OES based plasma data. For these purposes, this paper introduces a peak wavelength selection algorithm for selecting physically meaningful wavelength in plasma and discrete wavelet transform (DWT) for filtering out noisy signals from a CCD array. The effectiveness of the PCA model introduced in this paper is verified by comparing fault detection capabilities of conventional PCA model under the various source power or pressure faulty situations in a capacitively coupled plasma etcher. Even though the conventional PCA model fails to detect all of the faulty situations under the tests, the PCA model introduced in this paper successively detect even extremely small variation such as 0.67% of source power fault. The results introduced in this paper is expected to contribute to OES based plasma monitoring capability in plasma etching for more than Moore’s law era. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method for batch industrial process monitoring and fault detection. By combining time-slice dynamic prediction with an enhanced adaptive multiway principal component analysis (PCA) technique, it aims to balance the trade-off between high fault detection rates and low false alarm rates in monitoring batch industrial data. The method extracts dynamic features from 3D batch data time slices and utilizes a serial PCA framework with variable control limits for real-time monitoring. Comparative evaluations against traditional adaptive multiway PCA and dynamic time-slice canonical correlation analysis approaches demonstrate its superior performance. A case study using a novel cold rolling mill simulation model underscores its effectiveness for batch industrial process monitoring and fault detection. • Novel approach for dynamic extraction from batch industrial process data. • Time-slice monitoring and detection scheme based on serial PCA-KPCA. • Method validated on a novel cold rolling mill model. [ABSTRACT FROM AUTHOR]

Increased globalization, deregulation of energy markets, and environmental constraints, together with associated uncertainty, have created a highly dynamic and uncertain process manufacturing environment. Responding effectively to this increased variation and uncertainty is critical for a company to remain competitive. In this paper, we consider the plant infrastructure in relation to the energy and global market infrastructures. We describe changes to the plant infrastructure system in order to function more effectively in the current manufacturing environment, as well as key research advances that are aligned to addressing challenges faced by present day plant operation. • Discusses relationship between plant, energy, and global market infrastructures. • Identifies challenges associated with the current plant operating environment. • Discusses industry response to identified challenges. • Overview of PSE tools and paradigms aligned with addressing these challenges. • PSE approaches illustrated in the context of industrial air separation plants. [ABSTRACT FROM AUTHOR]

As manufacturing processes transition towards digitalization, data-driven process control is emerging as a key area of interest in future artificial intelligence technology. A crucial aspect in implementing data-driven process control is "What should we learn from the data?". In general, the data-driven control method can be categorized into two main approaches: Learning the model and learning the value. To assist in selecting the more suitable approach, this paper applies six different control methods, with three falling under each approach, to three distinct manufacturing process systems. The simulation results indicate that the model-learning approaches display higher data efficiency and exhibit lower variance in total cost. These methods prove to be particularly advantageous for addressing the regulation problems. Conversely, value-learning approaches show competitive potential in closed-loop identification and in managing economic cost problems. The remaining challenges associated with each technique are discussed, along with practical considerations for their implementation. • What should we learn, model or controller, with the collected data? • Quantitatively compare the model-based and model-free control approaches. • Model-based methods were superior in tackling regulation problems. • Model-free methods show competitive performance in optimizing economic cost. [ABSTRACT FROM AUTHOR]

• Summarize sustainability challenges and future trends of energy systems. • Analyze various development options and their impact on the power grid. • Propose multi-disciplinary research steps and challenges that must be addressed. The increased awareness of the climate change has triggered numerous actions and world-wide policies (e.g. the Paris agreement), with strong incentives to stop using fossil fuel-based energy forms in order to achieve a climate neutral economy (OECD, 2019). To realize the political targets, there are numerous challenging technical hurdles to overcome, which call for continued research. Earlier isolated systems need to come together and jointly collaborate to enable many changes in processes, for instance, seeking opportunities for electrification of energy intensive processes (Vogl et al., 2018). In this quest power systems play a crucial role through the increasing electricity transmission volumes, and they are already in the process of fundamental shift towards climate neutrality. The scale and urgency of this transformation is enormous and will affect most industries due to the central role of energy and electricity. In this paper, we discuss the main ongoing developments and future trends that also imply a closer coordination between power systems and various process systems. [ABSTRACT FROM AUTHOR]

The electrical energy (EE) price plays a significant role in the economic assessment of industrial processes. PSE/CAPE applications are often based on economic evaluations/optimizations where EE price and its possible dynamic evolution are important input data. Planning, scheduling, on-line optimization (dynamic) conceptual design, and feasibility studies are some of the applications where short-, medium-, and long-term predictions of EE quotations are involved. The paper discusses the main issues that contribute to EE quotations such as geographical, meteorological, seasonal, political, social, and financial terms. EE prices show a significant dependency on crude oil quotations with a time-delay of about a quarter/season. An econometric model comprising both linear and periodic components with an implicit stochastic term delivered by the reference component is first commented, then identified, and finally validated respect to real EE quotations. The paper provides also a discussion on how to forecast EE prices ranging from short- to long-term horizons. [ABSTRACT FROM AUTHOR]

The kernel of industrial advanced process control (APC) lies in the formulation and solution of model predictive control (MPC) problems, which specify the controller moves according to the solution of an optimal control problem at each sampling time. A significant challenge is the online computation for large-scale industrial systems. As the state-of-the-art APC technology, the Shell–Yokogawa Platform for Advanced Control and Estimation (PACE) has adopted a systematic framework of handling dynamic optimization of large-scale systems, where an automatic decomposition procedure generates subsystems for distributed MPC. The decomposition is implemented on network representations of the MPC models that capture interactions among process variables, with community detection used to maximize the statistical significance of the subnetworks with preferred internal interconnections. This paper introduces the fundamentals of such a decomposition approach and this functionality in PACE, followed by a case study on a crude distillation process to showcase its industrial application. • An industrial implementation of auto-decomposition for distributed MPC is reported. • The platform for advanced control of Shell–Yokogawa, PACE, is introduced. • A directed network captures structural dynamic interactions among process variables. • Community detection algorithm is practically refined to yield desirable subsystems. • Industrial-scale plantwide applications showcase the computational acceleration. [ABSTRACT FROM AUTHOR]

• This paper presents a retrospective comparison of seven representative dynamic latent variable (DLV) models for dynamic process monitoring. • This paper summarizes the essential contributions and problems of representative DLV methods and provides a significant reference for further development on DLV models. • This paper presents guidance on the selection of DLV models in practical application by evaluating the monitoring performance of the discussed approaches through a benchmark process. Process dynamic behaviors resulting from closed-loop control and the inherence of processes are ubiquitous in industrial processes and bring a considerable challenge for process monitoring. Many methods have been developed for dynamic process monitoring, of which the dynamic latent variables (DLV) model is one of the most practical and promising branches. This paper provides a timely retrospective study of typical methods to fill the void in the systematic analysis of DLV methods for dynamic process monitoring. First, several classical DLV methods are briefly reviewed from three aspects, including original ideas, the determination of parameters, and offline statistics design. Second, a discussion on the relationships of the discussed methods has been established to make a clear understanding of process dynamics explained by each method. Third, five cases of a three-phase flow process are provided to illustrate the effectiveness of the methods from the application viewpoint. Finally, future research directions on dynamic process monitoring have also been provided. The primary objective of this paper is to summarize the prevalent DLV methods for dynamic process monitoring and thus highlight a valuable reference for further improvement on DLV models and the selection of algorithms in practical applications. [ABSTRACT FROM AUTHOR]

This paper examines the prediction of disturbances based on their past measurements using k -nearest neighbours. The aim is to provide a prediction of a measured disturbance to a controller, in order to improve the feed-forward action. This prediction method works in an unsupervised way, it is robust against changes of the characteristics of the disturbance, and its functioning is simple and transparent. The method is tested on data from industrial process plants and compared with predictions from an autoregressive model. A qualitative as well as a quantitative method for analysing the predictability of the time series is provided. As an example, the method is implemented in an MPC framework to control a simple benchmark model. [ABSTRACT FROM AUTHOR]

In the process industry, various types of degradation occur in processing plants, resulting in significant economic losses. Modeling of degradation is important because it provides quantitative insights for consideration of degradation impacts in the operations of process manufacturing. This paper studies batch processes that show a periodic pattern for the evolution of degradation. A new data structure, the campaign, is applied for data-driven modeling of the periodic batch-to-batch evolution of degradation using a new multiway partial least squares approach, and it is further employed to predict the evolution of degradation in a series of batch runs. The proposed approach is illustrated and applied in a comprehensive industrial case study. The example illustrates the efficacy of the proposed model and presents a fair potential for applications of degradation prediction. [ABSTRACT FROM AUTHOR]

• Multi-objective planning under uncertainty using a continuous-time heuristic model. • Monte Carlo simulation is integrated with a variable-length multi-objective GA. • Monte Carlo simulation is accelerated with a Graphics Processing Unit (GPU). • The proposed approach is validated on an industrially-relevant case study. • The benefits of the approach are demonstrated by comparison with a deterministic GA. This paper presents a flexible genetic algorithm optimisation approach for multi-objective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequence-dependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput, and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multi-objective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation post-optimisation. [ABSTRACT FROM AUTHOR]

• A new deterministic model for WDN optimization is proposed considering unknown flow directions. • The minimum WDN cost model considers a set of available commercial diameters. • No hydraulic simulator is used for calculating pressures and velocities. • Disjunctive programming is used in the model formulation. • Global optimum solutions were obtained. Water Distribution Networks (WDN) are present in a large number of industrial processes and urban centers. Reservoirs, pipes, nodes, loops, and pumps compose WDN and their design can be formulated as an optimization problem. The main objective is the minimization of the network cost, which depends on the pipe diameters and flow directions known a priori. However, in the design of new WDN in real industrial problems, flow directions are unknown. In the present paper, a disjunctive Mixed Integer NonLinear Programming (MINLP) model is proposed for the synthesis of WDN considering unknown flow directions. Two case studies are employed to test the model and global optimization techniques are used in its solution. Results show that the global optima WDN cost with the correct flow directions is obtained for the studied cases without the necessity of using additional software to calculate pressure drops and velocities in the pipes. [ABSTRACT FROM AUTHOR]

Highlights • Upstream plantwide modelling and optimisation of the continuous pharmaceutical manufacturing (CPM) of atropine. • Continuous liquid–liquid extraction (CPM-LLE) design considers ternary phase composition modelling (UNIFAC) and estimated solute partitioning between product LLE phases. • Technoeconomic optimisation for comparative evaluation of separation solvents for CPM-LLE under varying design assumptions (plant capacity, solvent recovery). • Toluene emerges as the most economically viable CPM-LLE solvent choice compared to the batch LLE designs, followed by diethyl ether and n -butyl acetate. • Toluene usage allows acceptable E -factors for pharmaceutical processes. Abstract Continuous Pharmaceutical Manufacturing (CPM) can revolutionise industrial efficiency via potential operational and economic benefits over currently dominant batch methods. Process modelling and optimisation are valuable towards rapid design space evaluation and elucidation of optimal process design configurations, without expensive and time-consuming experimental campaigns. This paper pursues total cost minimisation via nonlinear optimisation of different separation design options for atropine, a product of great societal importance. The study considers a demonstrated continuous flow synthesis, presents reaction kinetic parameter estimation towards reactor design, and illustrates a comparative analysis of the subsequent batch vs. continuous liquid–liquid extraction (LLE) for product purification, using published partition coefficient data and UNIFAC-modelled ternary liquid–liquid equilibria. Original optimisation results show that toluene is the best continuous LLE solvent, attaining the lowest total costs at both plant capacities considered and the greatest total cost savings with respect to the batch LLE design for varying solvent recovery, at acceptable material efficiencies. [ABSTRACT FROM AUTHOR]

• Concept of a software tool optimized for hazard identification is demonstrated. • Semi-automatic HAZOP analysis was performed for an ammonia synthesis plant. • Steady state multiplicity was successfully identified in Aspen HYSYS environment. • An appropriate prediction of complex fault propagation paths was achieved. Process safety is of major importance in chemical industry. Numerous activities have targeted the modification of conventional risk assessment strategies by computer aided approach. In this paper, a software tool for Hazard and Operability (HAZOP) study based on process simulation is presented. Individual components of the proposed software tool are described and the principal methodology of their function is explained. As the simulation engine, commercial process simulator Aspen HYSYS was employed. Proposed tool was applied to a case study of an ammonia synthesis plant based on an existing plant. Hazardous events and operability problems in the syngas purification unit and ammonia synthesis loop have been detected and reported. The steady state multiplicity phenomenon in the ammonia synthesis loop has also been successfully identified. Based on simulation data evaluation performed in the semi-automatic manner by the proposed tool, a HAZOP-like report containing HAZOP deviations and their causes and consequences was generated. [ABSTRACT FROM AUTHOR]

Highlights • A novel data-driven model predictive controller for uni-axial rotomolding is proposed. • Subspace identification algorithm is used to identify the process model. • Proposed approach is implemented on an experimental setup. • Results demonstrate improved quality, variability rejection and ability to achieve product. Abstract This paper addresses the problem of achieving tight product consistency and enabling automated process changes to deliver user-selected criterion based product in a complex industrial batch process such as uni-axial rotational molding. To this end, a data driven state-space model is first identified. For a given trajectory of input moves (heater and compressed air profiles), this dynamic model is able to predict the evolution of the measured variable (internal product temperature). The dynamic model is augmented with a quality model, which relates its own terminal predictions to a selection of key quality variables (sinkhole area, ultrasonic spectra amplitude, impact test metric and viscosity). The dynamic and quality models are in turn utilized within a model predictive control (MPC) framework that enables specifying product quality requirements explicitly. Experimental results demonstrate the ability of the MPC not only in achieving tight quality control but also providing on-spec product for a new specification. [ABSTRACT FROM AUTHOR]

Highlights • Simulation study to economics optimizing control and robust multi-stage optimizing control. • Discussion of numerical aspects of optimizing control as well as the software realization using high-fidelity high-dimensional process models of complex chemical processes. • Control problem formulation for the dynamic product changeover in a multi-product process. • Robustification of economics optimizing control by multi-stage nonlinear model predictive control in case of the presence of model uncertainty. Abstract By the use of online optimization, the profitability of chemical processes can be enhanced while meeting process-related, environmental and ecological constraints. Two well-known techniques to achieve an economically optimal operation of chemical processes are repetitive stationary optimization (RTO) combined with advanced control and direct economics optimizing control on a receding horizon (also called one-layer approach or dynamic RTO). Direct economics optimizing control can react faster to disturbances and is better suited for the optimization of transients between different products. On the other hand it is computationally demanding and requires sufficiently accurate dynamic models. In this paper, we discuss economics optimizing control of a very complex process, a multi-product transesterification reaction that is realized in a reactive distillation process. The process model, the specification of the economics optimizing controller, the implementation of the dynamic optimization, and the robustification of the controller by a multi-stage approach are discussed using simulation studies. [ABSTRACT FROM AUTHOR]

Highlights • A novel piecewise state feedback controller is introduced. • The chemical process optimal control problem is formulated as an optimal control problem of switched dynamical systems. • A gradient-based parallel optimization algorithm is developed for solving the optimal control. • Convergence results indicate that the gradient-based parallel optimization algorithm is global convergent. • Three chemical process optimal control problems are given to illustrate the effectiveness of the proposed algorithm. Abstract This paper considers an optimal control problem of chemical processes with a novel piecewise state-feedback controller. Firstly, the chemical process optimal control problem is formulated as a switched dynamical system optimal control problem, which can be transformed into a parameter optimization problem. Next, to achieve rapid convergence from remote starting points, we propose a novel gradient-based optimization algorithm, which is suitable to a parallel implementation because the step is improved by updating its direction as well as its length simultaneously before moving to the next iteration, and the step computation involves only the inner products of vectors. Then, the convergent properties of the parameter optimization problem to the original optimal control problem are discussed. Finally, the numerical simulation results show that the gradient-based parallel optimization algorithm is an effective alternative method for solving the chemical process optimal control problems. [ABSTRACT FROM AUTHOR]

Fault diagnosis (FD) has received attention because of its importance in maintaining safe operations of industrial processes. Recently, modern data-driven FD approaches such as deep learning have shown encouraging performance. Particularly, convolutional neural networks (CNNs) offer an alluring capacity to deal with multivariate time-series data converted into images. Nonetheless, existing CNN techniques focus on capturing local correlations. However, global spatiotemporal correlations often prevail in multivariate time-series data from industrial processes. Hence, extracting global correlations using CNNs from such data requires deep architectures that incur many trainable parameters. This paper proposes a novel local–global scale CNN (LGS-CNN) that directly accounts for local and global correlations. Specifically, the proposed network incorporates local correlations through traditional square kernels and global correlations are collected utilizing spatially separated one-dimensional kernels in a unique arrangement. FD performance on the benchmark Tennessee Eastman process dataset validates the proposed LGS-CNN against CNNs, and other state-of-the-art data-driven FD approaches. • A novel convolutional neural network is proposed for complex fault diagnosis tasks. • The proposed model can handle both local and global spatiotemporal correlations. • The ability to capture global spatiotemporal dependencies is mathematically analyzed. • A closer look at the local receptive fields of the proposed model is provided. • An evaluation against a group of data-driven fault diagnosis techniques is included. [ABSTRACT FROM AUTHOR]

Value chain integration is an ongoing challenge: while computing power has improved, there is little modeling consistency across the system. This paper bridges this gap by proposing a novel formulation for train scheduling, a linking element of value chains, using the Unit-Operation-Port-State Superstructure (UOPSS). Train scheduling is a challenging problem: rail lines can be hundreds of kilometers long with train crossing strategies that are based on a train station level, while also requiring results with a minute-time scale resolution. In mixed-use rail systems with limited passing loop infrastructure, trains have different passing priorities and lengths, thus differing in their ability to use passing loops. The proposed model is the basis of the Hatch Rail Optimizer (HRO) software. In addition to small case studies, the power of HRO is demonstrated through a practical case study involving a 370 km rail corridor with five different train sizes over a week-long scheduling horizon. [ABSTRACT FROM AUTHOR]

This paper presents a fault detection method based on augmented kernel Mahalanobis distance (AKMD) for monitoring nonlinear dynamic processes. In order to reflect the information of dynamic correlations, the measurements are stacked into augmented vectors at adjacent sampling instants. The augmented kernel Mahalanobis distance serves as the detection index, and its control limit is determined by the empirical method with assigning a significance level. Contrary to the mainstream of process monitoring methods based on principal component analysis (PCA), dimensionality reduction is not used here. The disadvantage of dimensionality reduction and space partition is discussed, and the improvement of fault detectability via data augmentation is analyzed. In addition, the computational complexity of the proposed method is acceptable. For training dataset containing m variables and n samples, if n ≫ m , the online computational burden of the proposed method is about O ( n 2 ) . Simulations about a nonlinear dynamic process and the benchmark Tennessee Eastman process (TEP) both illustrate higher detection rates of the proposed method, compared with conventional multivariate statistical process monitoring (MSPM) methods such as PCA and its variants. [ABSTRACT FROM AUTHOR]

• Present a detailed review on manufacturing system maintenance operations in the downstream petroleum industry. • Identify the policies, optimization methodologies, implementation frameworks, and other maintenance efficiency enhancement efforts in the industry to provide general guidance for practitioners and researchers. • Organize maintenance optimization approaches into two broad categories, i.e., based on the criticality of the equipment and based on tools. • Track the recent trends in the maintenance implementation approach, identify gaps, and recommend future research directions to advance the industry's maintenance functions. This paper presents a literature review on maintenance operations in the downstream petroleum industry. This process industry comprises facilities ranging from processing units to distribution networks, which makes the maintenance activities diverse. Maintenance optimization approaches from over 120 articles have been organized into two broad categories. The first category implemented maintenance operations according to the criticality of equipment by applying methods like American Petroleum Institute, Analytical Hierarchy Process, and Failure Modes and Effect Analysis. The second category applied various optimal policies by adopting different tools such as mathematical models (probability, statistics, linear or nonlinear optimization methods, fuzzy logic), heuristic (metaheuristics) algorithms (genetic algorithm, firefly algorithm), data analytics (machine learning), and Internet of Things. The review also included maintenance implementation frameworks, planning & scheduling methods, safety, mechanization, and evaluation procedures. It also tracks the recent trends in the maintenance implementation approach, identifies gaps, and recommends future research directions. [ABSTRACT FROM AUTHOR]

Detrended Fluctuation Analysis (DFA) is a reliable and assumption-free approach for gauging the complexity of a time series. In this paper, an online oscillations detection paradigm is presented, which integrates the potential of DFA in detecting abnormal coherent fluctuations with the Empirical Wavelet Transform (EWT) efficiency in extracting the characteristics of oscillations. However, the standard EWT fails to separate modes oscillating at close frequencies, resulting in an incorrect decomposition. Furthermore, the lack of an appropriate stopping criterion frequently results in the signal being over-decomposed into several inconsequential components. Therefore, owing to the capability of DFA to differentiate between fluctuations stemming from noise and coherent fluctuations arising from genuine oscillations, an Improved EWT (IEWT) is presented to mitigate these issues and accurately extract only compelling oscillating modes. The proposed DFA-based IEWT framework is verified on simulated applications and data from real industrial processes, illustrating its effectiveness. • An integrated monitoring scheme using DFA and IEWT for online oscillations detection. • Oscillations characterisation based on IEWT for industrial control loops. • This method works effectively under different noise density levels. • Incipient oscillations are detected efficiently at their early stage. • The proposed IEWT outperforms the standard EWT in decomposing the signals. [ABSTRACT FROM AUTHOR]

• This paper analyzes the interconnection of fault detection and diagnosis, risk assessment and abnormal situation management. • The paper reviews the existing methods and models related to fault detection and diagnosis, abnormal situation management, and risk. • The paper also present authors' thoughts on the research direction in the area of process safety in industrial 4.0. • This paper aims to serve as a road map for process safety research to enable safer and sustainable development. Industry 4.0 provides substantial opportunities to ensure a safer environment through online monitoring, early detection of faults, and preventing the faults to failures transitions. Decision making is an important step in abnormal situation management. Assigning risk based on the consequences may provide additional information for abnormal situation management decisions to prevent the accident before it occurs. This paper analyzes the interconnections between the three essential aspects of process safety: fault detection and diagnosis (FDD), risk assessment (RA), and abnormal situation management (ASM) in the context of the current and next generation of process systems. The authors present their thoughts on research directions in process safety in Industry 4.0. This article aims to serve as a road map for the next generation of process safety research to enable safer and sustainable process operations and development. [ABSTRACT FROM AUTHOR]

Principal component regression (PCR) has been widely used as a multivariate method for data-based soft sensor design. In order to take advantage of probabilistic features, it has been extended to probabilistic PCR (PPCR). Commonly, industrial processes operate in multiple operating modes. Moreover, in most cases, outputs are measured at a slower rate than inputs, and for each sample of input variable, its corresponding output may not always exist. These two issues have been solved by developing the mixture semi-supervised PPCR (MSPPCR) method. In this paper, we extend this developed model to the case of simultaneous missing data in both input and output. Missing data in multidimensional input space constitutes a significantly more challenging problem. Missing input data occurs frequently in industrial plants because of sensor failure and other problems. We develop and solve the MSPPCR model by using the expectation-maximization (EM) algorithm to deal with missing inputs, in addition to missing outputs and multi-mode conditions. Finally, we present two case studies to demonstrate its performance. [ABSTRACT FROM AUTHOR]

This paper presents a multi-compartment population balance model for wet granulation coupled with DEM (discrete element method) simulations. Methodologies are developed to extract relevant data from the DEM simulations to inform the population balance model. First, compartmental residence times are calculated for the population balance model from DEM. Then, a suitable collision kernel is chosen for the population balance model based on particle–particle collision frequencies extracted from DEM. It is found that the population balance model is able to predict the trends exhibited by the experimental size and porosity distributions by utilising the information provided by the DEM simulations. [ABSTRACT FROM AUTHOR]

• Surrogate models have been developed for the design of tablet manufacturing. • Random forest regression was chosen for dissolution behavior prediction. • Case studies were performed for both dry and wet granulation methods. • Developed models can reduce the calculation time with high accuracy. • Critical input and intermediate parameters were found by sensitivity analysis. This paper presents surrogate models of dissolution behavior to identify the critical input parameters in tablet manufacturing processes. Dissolution behavior is a critical quality attribute, and is defined as the time profile of fraction of dissolved active pharmaceutical ingredients in a solvent from a tablet. Dissolution behavior was first calculated by mechanistic models using gPROMS® and fitted by the Weibull model. Random forest regression calculated the Weibull model parameters from the input model parameters. The developed models enabled to reduce computational cost without losing accuracy. Case studies were performed for both dry and wet granulation processes using paracetamol. The results identified key input and intermediate parameters to the dissolution behavior, e.g., granule bulk density and porosity. In addition, wet granulation had a tendency to make dissolution faster than dry granulation. The proposed approach can contribute to enhance simulation capabilities in the limited time of pharmaceutical process development. [Display omitted] [ABSTRACT FROM AUTHOR]

The integration of data-driven modeling techniques in machine learning applications, such as multiclass classification, has resulted in robust classifier designs. However, one of the main drawbacks of this approach has been the rising complexity of modeling as the number of classes in the system increases, which may eventually make the overall design of the classifier unfavorable regardless of the expected performance. In this paper, we will discuss the design of a novel logic-based Bayesian-optimized Gaussian process (BOGP) classifier that aims to minimize the number of independent empirical models needed to accurately diagnose multiple distinct fault classes in industrial process. Moreover, the fault classification accuracy of the BOGP classifier is compared to the respective performances of other methods published in literature, and the Tennessee Eastman process is used as a benchmark case study for all methods. • Accurate fault classification is crucial for operational safety and maintenance. • Multi-class systems drive the need for compact and adaptive classifier designs. • Bayesian-optimized Gaussian process classifier proposed to diagnose faults. • Tennessee Eastman industrial process used as benchmark case study. [ABSTRACT FROM AUTHOR]

Optimisation of cell culture processes is an extremely challenging problem for the biomanufacturing industry. Limited data availability, coupled with biological complexity in modelling highly variable living cells, necessitates a decision support methodology that is performant under high levels of uncertainty. Raw materials, experimental & manufacturing facilities, and human expertise are all steeply expensive and availability is tightly constrained — planning their allocation is subject to the core uncertainties underlying the behaviour of living cells. This paper presents a novel decision support methodology for optimisation under high uncertainty. Optimisation techniques require an "objective function" that maps decision variables to the quantity being optimised, so that the decision space can be explored to find an optimum. By learning multiple types of objective function candidates with different levels of fidelity to real-world processes, our method mitigates the risk of picking a poor approximation of the objective function due to sampling effects and algorithmic randomness. Wet lab experimentation on a biomanufacturing feed optimisation problem verified that inferred candidates can successfully support domain experts in designing a new optimised feed strategy with significantly higher product yield than the current industrial control strategy. Our results indicate potential for extending our methodology to the optimisation of other complex industrial processes. • Populations of Objective Functions to mitigate uncertainty. • Machine Learning to Learn objective function. • Optimising Product Yield for manufacturing bioprocesses. • Real world validation. [ABSTRACT FROM AUTHOR]

In this paper, we seek a tighter coordination between the production planning and process control layers in the process decision-making hierarchy, with the purpose of reducing the economic gap between planned and realized production. We introduce deep learning-based frameworks that support a stochastic production planning (PP) formulation that integrates economic model predictive control (EMPC) and PP via a feedback mechanism that relays the effect of process disturbances (traditionally dealt with by the control layer) to the planning layer. Our framework demonstrates superior economic performance, promising solution times (potentially allowing industrial implementation) and a close match of planned and realized process economics compared to a traditional industrial linear and non-integrated EMPC-PP benchmark. • A deep learning/hybrid EMPC structure is presented. • An interpretable, integrated EMPC and production planning framework is developed. • Framework is demonstrated on a representative large-scale process. [ABSTRACT FROM AUTHOR]

In response to the continuously changing feedstock supply and market demand for products with different specifications, industrial processes need to be operated at time-varying operating conditions and targets (e.g., setpoints) to improve their economy, in contrast to traditional process operations around predetermined equilibriums. In this paper, a novel control approach is developed for nonlinear chemical processes to achieve time-varying reference tracking, by combining the universal approximation characteristics of neural networks with the rigor of discrete-time contraction analysis and control. This is a robust approach that can deal with bounded parametric uncertainties in the process model, which are commonly encountered in industrial processes. A new synthesis approach is developed that involves training a neural network representation of a contraction metric and differential feedback gain for a full range of parametric uncertainty. This network is then embedded in a contraction-based control structure. A separate neural network is also incorporated into the control-loop to perform online identification of uncertain system model parameters. The resulting control scheme is capable of achieving efficient offset-free tracking of time-varying references under the parametric uncertainty, without the need for controller redesign as the reference changes. Process stability is also ensured during online simultaneous learning and control. Simulation examples are provided to illustrate the approach. • A new contraction metric and control synthesis approach using neural networks. • A systematic method to ensure stability for neural network-based nonlinear control. • Certificates for robust stabilizability to time-varying references. • Safe online iterative learning of uncertain system parameters. [ABSTRACT FROM AUTHOR]

Although modern chemical processes are highly automatic, abnormal situation management (ASM) still heavily relies on human operators. Process fault detection and diagnosis (FDD) are one of the most important issues of ASM but few FDD systems have been satisfactorily applied in real chemical processes since the concept of FDD was proposed about 40 years ago. In this paper, developments of chemical process FDD are briefly reviewed. The reason why FDD has not been widely implemented in the chemical process industry is discussed. One of the insights gained is that some basic problems in FDD such as how to define faults and how many faults to diagnose have not even been addressed well while researchers tirelessly try to invent new methods to diagnose fault. A new framework is proposed based on the big data in a cloud computing environment of a big chemical corporation for addressing the challenging issues in ASM. [ABSTRACT FROM AUTHOR]

PSE, process systems engineering, is about the development and application of systematic methods for process studies by the chemical engineer. By means of software tools, the application of these methods is facilitated. Over the last about half a century, CAPE (computer aided process engineering) tools have found their way into process engineering. For example it is unthinkable nowadays to design a plant without a simulation through a process simulator. But there are many more applications of PSE in industry. The aim of this paper is to provide a taste of the meaning of PSE within the industrial R&D environment. The intention is not to provide a complete overview but to give a flavour of what is perceived as the benefits of PSE during process development, and, in which areas PSE should be extended to render further benefits. The combined approach of experiments and modelling offers a very (cost-)effective strategy in industrial R&D. Further improvements are desired in the areas related to process intensification (PI) and (conceptual) product design. It is believed that the current methods would be more beneficial and have a stronger applicability in industry by inclusion of semi-predictive models and uncertainty considerations. [ABSTRACT FROM AUTHOR]

The paper presents a dropwise additive manufacturing process for pharmaceutical products (DAMPP) as an alternative to conventional methods. This mini manufacturing process for the production of personalized pharmaceutical products utilizes drop-on-demand (DoD) printing technology for the deposition of active pharmaceutical ingredient (API) onto edible substrates. Here we present a process control framework for DAMPP, including on-line monitoring, automation and closed loop control, in order to produce individual dosage forms with the desired critical quality attributes, including formulation composition, drop size, deposit morphology and dissolution performance. In order to achieve desired product morphology, a surrogate model based on polynomial chaos expansion is developed to relate the critical process parameters to deposit morphology using dissolution data of the active pharmaceutical ingredient. The proposed process control strategy can effectively mitigate variations in the dissolution profiles due to variable dosage amounts and enable the application of the DoD system for the production of individualized dosage regimens. [ABSTRACT FROM AUTHOR]

Multi-objective constrained optimization problems which arise in many engineering fields often involve computationally expensive black-box model simulators of industrial processes which have to be solved with limited computational time budget, and hence limited number of simulator calls. This paper proposes two heuristic approaches aiming to build proxy problem models, solvable by computationally efficient optimization methods, in order to quickly provide a sufficiently accurate approximation of the Pareto front. The first approach builds a multi-objective mixed-integer linear programming (MO-MILP) surrogate model of the optimization problem relying on piece-wise linear approximations of objectives and constraints obtained through brute-force sensitivity computation. The second approach builds a multi-objective nonlinear programming (MO-NLP) surrogate model using curve fitting of objectives and constraints. In both approaches the desired number of approximated solutions of the Pareto front are generated by applying the ɛ -constraint method to the multi-objective surrogate problems. The proposed approaches are tested for the cost vs. life cycle assessment (LCA)-based environmental optimization of drinking water production plants. The results obtained with both approaches show that a good quality approximation of Pareto front can be obtained with a significantly smaller computational time than with a state-of-the-art metaheuristic algorithm. [ABSTRACT FROM AUTHOR]

• The definition and characteristics of smart fault detection and diagnosis are elaborated. • The four major challenges to implementing smart fault detection and diagnosis are addressed. • Recent advances related to smart fault detection and diagnosis are systematically reviewed. • A general outlook on the future of process safety is discussed. Process fault detection and diagnosis (FDD) is an essential tool to ensure safe production in chemical industries. After decades of development, despite the promising performance of some FDD methods on specific tasks, most FDD methods are not smart enough to tackle the complex challenges in real industrial processes, rendering an absence of commercialized FDD tools. Therefore, the implementation of smart FDD becomes an ambitious goal for process safety. In this paper, we provide an overview of the concept and major challenges of smart FDD. Recent FDD methods are comprehensively evaluated with respect to the characteristics of smart FDD. We also present the researches done by our group, which we believe would be a step forward for smart FDD. A range of future opportunities and new perspectives are further discussed. This review aims to illuminate potential directions for process safety and to contribute to the realization of commercial FDD tools. [ABSTRACT FROM AUTHOR]

• An unsupervised fault detection and diagnosis scheme based on orthogonal autoencoders is proposed for the monitoring of industrial and chemical processes. • The use of integrated gradients allows for exploration of the bottleneck of the autoencoder, providing candidate variables for the root cause analysis. • The proposed method performs well compared to traditional methods in offering compelling and interpretable results. • The analysis shows how the difference in the way faults are introduced affects the detection and diagnosis performances of principal component analysis-based control charts. In recent years, there have been studies focusing on the use of different types of autoencoders (AEs) for monitoring complex nonlinear data coming from industrial and chemical processes. However, in many cases the focus was placed on detection. As a result, practitioners are encountering problems in trying to interpret such complex models and obtaining candidate variables for root cause analysis once an alarm is raised. This paper proposes a novel statistical process control (SPC) framework based on orthogonal autoencoders (OAEs). OAEs regularize the loss function to ensure no correlation among the features of the latent variables. This is extremely beneficial in SPC tasks, as it allows for the invertibility of the covariance matrix when computing the Hotelling T 2 statistic, significantly improving detection and diagnosis performance when the process variables are highly correlated. To support the fault diagnosis and identification analysis, we propose an adaptation of the integrated gradients (IG) method. Numerical simulations and the benchmark Tennessee Eastman Process are used to evaluate the performance of the proposed approach by comparing it to traditional approaches as principal component analysis (PCA) and kernel PCA (KPCA). In the analysis, we explore how the information useful for fault detection and diagnosis is stored in the intermediate layers of the encoder network. We also investigate how the correlation structure of the data affects the detection and diagnosis of faulty variables. The results show how the combination of OAEs and IG represents a compelling and ready-to-use solution, offering improved detection and diagnosis performances over the traditional methods. [ABSTRACT FROM AUTHOR]

• Two-layer model predictive energy management system for industrial batch processes. • Modular formulation of the arising mixed-integer linear program with simple parameterization. • Online load predictor for thermal batch processes ensuring robustness against uncertainties. • Performance demonstration and comparison on an existing food factory. Predictive energy management systems (EMS) enable industrial plants to participate in the modern power market and reduce energy cost. In this paper, a novel modular model predictive EMS specifically designed for industrial thermal batch processes is presented. The EMS consists of a two-layer mixed-integer model predictive controller and an online load predictor, and thus solves the main challenges of EMS in industry - high implementation costs and the possible reduction of production reliability. The modular formulation of the optimization problem enables system integrators to implement the EMS without time-consuming modelling tasks and elaborate parameter tuning. The online load predictor estimates the typical pulse-like heat loads of batch processes ensuring both - reliable production and maximal flexibility of the power demand. The utilization of real-time data provides additional robustness against uncertainties caused by human operators. The performance of the EMS is evaluated in a case study of an existing food plant. [ABSTRACT FROM AUTHOR]

• A new method is proposed to analyze time-varying cause-effect relations. • Key and influence variables are observed with related qualitative trends. • Time delays are estimated by aligning qualitative trends of process variables. • Contributions of influence variables are quantified from amplitude changes. • Numerical and industrial case studies are provided for demonstration. Determination of root causes for occurring abnormalities is a sophisticated work in process monitoring of complex process industries. Many existing methods are based on an assumption that causal-effect relations are time invariant, whereas real industrial processes may have complex dynamic characteristics, leading to time-varying casual-effect relations. In this paper, a new cause-effect detection method is proposed to analyze time-varying cause-effect relations between the key variable and influence variables. This study is motivated by a common observation in practice that if the key variable takes an increasing/decreasing amplitude change, then corresponding influence variables are with amplitude changes in the same/opposite qualitative trends, and such relations between trend changes may change with time. The proposed method consists of three main steps: i) the time series of the key variable is represented by short straight lines to formulate piecewise linear representation (PLR) segments, ii) qualitative trends of involved variables are extracted on the basis of PLR segments, after estimating time delays between influence variables and the key variable, iii) contribution factors of influence variables to changes of the key variable are calculated by building a multiple linear regression model for normalized amplitude changes of involved variables. The effectiveness of the proposed method is demonstrated by numerical and industrial case studies. [ABSTRACT FROM AUTHOR]

• Addressing the control performance assessment problem using Hurst index. • Examining crossover phenomena in estimating the Hurst exponent of control signals. • Modifying the Hurst index based on the crossover phenomena. • Estimating the minimum variance index using routine operation data of the system. This paper proposes a new Hurst exponent-based method to evaluate the performance of control loops by estimating Minimum Variance Index (MVI). Incorrect estimation of time delays dramatically affects MVI-based performance assessment accuracy. The main advantage of the proposed method over existing methods is that it only requires routine operation data, without any need for prior information of the system model parameters and time delays. Hence, this method is more suitable to be applied on various industrial control processes. Recently, crossover phenomena have been reported in the graphs used to estimate the Hurst exponent of different control signals, which complicate deciding the Hurst exponents values. To address this problem, the crossover phenomenon is investigated for control signals, then the Hurst index is modified based on it. The proposed method is successfully used to assess performance of various types of control loops such as the cascade loop and verified through simulation results. [ABSTRACT FROM AUTHOR]

• Scanning historical data from industrial processes with the purpose of finding useful intervals for system identification. More specifically, it considers the problem in its multivariable version, for a system under closed-loop control. • For sufficient exciting set-points, we can expect to obtain reasonably unbiased models. We can also reinforce such a scenario by selecting strict thresholds for the numerical conditioning criteria. • The resulting algorithm is applied to seven months of historical data from a petrochemical furnace of the Brazilian company Petrobras. • Two different approaches were considered: the first one uses the condition number to assess the intervals numerical conditioning and performs a chi-squared test to check the signals correlation; the second one uses the effective rank and a scalar cross-correlation metric to accomplish the same task. • The intervals quality can be verified not only through the segmentation methods metrics, but also through direct visualization and through the resulting system identification metrics. This paper addresses the complex problem of scanning historical data from industrial processes with the purpose of finding useful intervals for system identification. More specifically, it considers the problem in its multivariable version, for a system under closed-loop control. A detailed review of the literature on the subject is presented and some of the studied methodologies are condensed into an algorithm. The resulting algorithm is applied to seven months of historical data from a petrochemical furnace of the Brazilian company Petrobras. The objective of the algorithm is to obtain process models that represent the dynamics of the system between the set-points and the output variables with the goal of designing predictive controllers. A comparison of different techniques is presented, elucidating their impacts on the results in the furnace dataset. In addition, a viable way of choosing parameters is presented, allowing the use of the algorithm in massive data. [ABSTRACT FROM AUTHOR]