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Primary drying is the bottleneck of pharmaceutical lyophilization, as it represents the most time and energy-consuming stage and largely determines the quality of the final product. The common practice runs the process under pre-calculated input profiles while considering average drying dynamics for the batch. This results in conservative and underperforming cycles. In this regard, the paper proposes and experimentally validates an in-line control strategy for primary drying. It explicitly accounts for intra-lot drying heterogeneity using three models, each one calibrated for a reference vial position in the chamber. Two separate experiments evaluate the proposed framework: offline and in-line process control. Validation on a pilot-scale unit shows a cycle time reduction of the proposed approaches compared to standard drying protocol at constant operating conditions. The input profiles obtained by offline control lead to a 16% shorter cycle time; however, the lack of feedback during the cycle impedes addressing potential parametric variations and model mismatches. Conversely, in-line control yields a reduction of 13% while allowing careful monitoring of the product status, ensuring a more dependable cycle. In both cases, the visual quality inspection confirms the acceptability of the final product without visible degradation. [Display omitted] • A control strategy for multi-vial control of primary dying that can accommodate any number of vials. • Three representative vial positions considered: center, edge and corner. • Application of offline control yields a 16% cycle reduction but can be susceptible to disturbances. • Application of in-line control in a pilot unit yields the safest cycle and a time reduction of 13%. [ABSTRACT FROM AUTHOR]

In 1969, in the name of development modernity and Canadian unity, the government of Pierre Elliott Trudeau undertook the most extensive land expropriation in the history of the country, to build the largest airport in the world, Mirabel. The Canadian government expropriated approximately twelve thousand people and ninety‐seven thousand acres of land for the project. Mirabel was a dramatic failure, for social, political, and economic reasons. This article focuses on the development discourse that the state used to promote its ambitions, the relation that expropriated farmers had to their private property, and the slow but eventually strong and successful resistance of owners whose lands the state requisitioned. [ABSTRACT FROM AUTHOR]

Recently, a GPU-based multicriteria optimization (gMCO) algorithm was integrated in a graphical user interface (gMCO-GUI) that allowed real-time plan navigation through a set of Pareto-optimal plans for high-dose-rate (HDR) brachytherapy. This work reports on the inter-observer evaluation of the gMCO algorithm into the clinical workflow. Twenty HDR brachytherapy prostate cancer patients were retrospectively replanned with the gMCO algorithm. The reference clinical plans were each generated by experienced physicists using inverse planning followed by graphical optimization and approved by a radiation oncologist (RO). Each case was replanned with the gMCO algorithm by generating 2000 Pareto-optimal plans with four different objective functions. Two physicists were asked to rank the objective functions according to their preferences by choosing one preferred plan for each plans pool and ranking them using gMCO-GUI. The optimized dwell positions and dwell times of the gMCO plans that were ranked first were exported to Oncentra Prostate where a blinded comparison of the gMCO plans with the clinical plans was conducted by three ROs. The median planning time of the two physicists was 9 min. Both physicists preferred the objective function with target sub-regions to cover specific target regions. Regarding the blinded comparison, the gMCO plans were preferred 19, 17, and 12 times by the three ROs, in which eight gMCO plans were unanimously preferred compared with the clinical plans. The plan quality and the planning time were similar between the two physicists and within what is observed in the clinic. Moreover, the gMCO plans evaluated favorably by ROs compared to the reference clinical plans. [ABSTRACT FROM AUTHOR]

• Introduction of a control strategy for batch tablet film-coating processes. • Effective use of available process data for online control of critical variables. • Use of a dynamic process simulator to validate a decentralized PI controller. • Potential reduction in total batch processing time and energy consumption. This paper proposes a control strategy for batch tablet film-coating processes using readily available process instrumentation. Outlet air conditions (temperature and relative humidity), which impact coating quality, are controlled by the manipulation of inlet air temperature and coating spray rate. Controller design, tuning and validation rely on the use of a dynamic process simulator, calibrated using data collected from a pilot plant coating unit. The final 2 × 2 decentralized PI controller demonstrated good performance for both setpoint tracking and disturbance rejection in simulation. Additionally, controlled variable setpoints were determined to maximize plant profits considering batch duration and energy consumption as optimization criteria. For the case study considered, the total batch processing time can potentially be reduced by 40 % while simultaneously reducing energy usage by 30 %, in comparison with the more conservative fixed recipe approach typically adopted in production. [ABSTRACT FROM AUTHOR]

Currently in high-dose-rate (HDR) brachytherapy planning, manual fine-tuning of an objective function is a common practice. Furthermore, automated planning approaches such as multicriteria optimization (MCO) are still limited to the automatic generation of a single treatment plan. This study aims to quantify planning efficiency gains when using a graphics processing unit–based MCO (gMCO) algorithm combined with a novel graphical user interface (gMCO-GUI) that integrates efficient automated and interactive plan navigation tools. The gMCO algorithm was used to generate 1000 Pareto optimal plans per case for 379 prostate cases. gMCO-GUI was developed to allow plan navigation through all plans. gMCO-GUI integrates interactive parameter selection tools directly with the optimization algorithm to allow plan navigation. The quality of each plan was evaluated based on the Radiation Treatment Oncology Group 0924 protocol and a more stringent institutional protocol (INSTp). gMCO-GUI allows real-time time display of the dose–volume histogram indices, the dose–volume histogram curves, and the isodose lines during the plan navigation. Over the 379 cases, the fraction of Radiation Treatment Oncology Group 0924 protocol valid plans with target coverage greater than 95% was 90.8%, compared with 66.0% for clinical plans. The fraction of INSTp valid plans with target coverage greater than 95% was 81.8%, compared with 62.3% for clinical plans. The average time to compute 1000 deliverable plans with gMCO was 12.5 s, including the full computation of the 3D dose distributions. Combining the gMCO algorithm with automated and interactive plan navigation tools resulted in simultaneous gains in both plan quality and planning efficiency. [ABSTRACT FROM AUTHOR]

Data reconciliation is extensively applied to improve the accuracy and reliability of plant measurements. It relies on process models ranging from simple mass and energy conservation equations to complete causal models. The precision of reconciled data mainly depends on the complexity and quality of plant models used to develop data reconciliation observers. In practice, the difficulty of obtaining detailed models prevents the application of powerful observers like the Kalman filter. The objective of this study is to propose a methodology to build a model for a flotation circuit to support the implementation of a Kalman filter for dynamic data reconciliation. This modeling approach extracts essential information from the plant topology, nominal operating conditions, and historical data. Simulation results illustrate that applying a Kalman filter based on a rough empirical model that has been correctly tuned gives better estimates than those obtained with sub-model based observers. [ABSTRACT FROM AUTHOR]

Data reconciliation is widely applied in mineral and metal processing plants to improve information quality. Imprecision, unreliability and incompleteness of measurements are common problems motivating the implementation of the technique. Current practices rely on mass and energy conservation constraints to estimate the underlying steady-state values of process variables. Typically, the Gaussian context is assumed and a Maximum-Likelihood estimator is selected. The performance of such an estimator depends on the covariance matrices used to characterize model and measurement uncertainties. In practice, determining these covariance matrices is a challenging task that is often overlooked. Using inappropriate uncertainty models, based on simplistic or improper hypotheses, can lead to unexpected underperformances. The objective of the paper is to illustrate the impact of correctly selecting uncertainty covariance matrices for steady-state data reconciliation. Different case-studies involving a combustion chamber, a hydrocyclone, a flotation circuit, and a separation unit are used for investigating the sensitivity of the algorithm to the structure of covariance matrices. An example based on Monte-Carlo simulations is presented to assess the importance of assigning right values to variance terms. Simulation results show that the adjustment of uncertainty covariance matrices has a significant influence on the precision of estimates and reveal that some common tuning practices can have detrimental effects. [ABSTRACT FROM AUTHOR]

Abstract: Data reconciliation is a well-known technique to improve accuracy and reliability of plant measurements. It relies on process models that could range from simple mass and energy conservation equations to complete causal dynamic models. Generally, precise estimates imply detailed plant models that could be difficult to build and update in practice. The trade-off between modeling efforts and estimation performances has thus lead to various approaches to deal with plant dynamics. The objective of the paper is to review and compare most common observers used for dynamic data reconciliation in the mineral and metallurgical processing industries. Comparisons are carried out using a separation unit and a flotation circuit as simulated benchmark plants. Observer performances are evaluated in terms of variance reduction. Strengths and weaknesses of the different methods are highlighted. Aspects such as estimation of model parameters, detection of gross errors, and handling of bilinear equations and plant non-linearities are discussed. [Copyright &y& Elsevier]

Abstract: To reduce impacts of measurement errors on plant variables, data reconciliation is widely applied in process industries. Reconciled measurements are used in applications such as performance monitoring, process control, or real-time optimization. However, precise estimation generally relies on accurate and detailed process models which could be difficult to build in practice. The trade-off between estimate precision and model complexity is a relevant challenge motivating the development of effective observers with limited modeling efforts. This paper proposes a data reconciliation method based on a simple mass and/or energy conservation sub-model that also considers the autocovariance function of plant node imbalances. The observer is applied to simulated benchmark plants and its performance is evaluated in terms of variance reduction and robustness against modeling errors. Results show a superior performance in comparison with classical sub-model based methods and reveal less performance degradation than the Kalman filter in presence of model uncertainties. [Copyright &y& Elsevier]

This paper proposes a development to reformulate the fundamental equations of primary drying into a linear-based representation to support control design. The model is expressed by transfer functions with variable gains that incorporate analytical relations derived from a phenomenological model. The time constants denote the dynamics of the heating/cooling and vacuum systems. Compared to fundamental equations, this approach simplifies the development and implementation of well-established control algorithms. The quality of the proposed representation is illustrated in the design of a control system using two different strategies: model predictive control and a feedback controller with proportional-integral action. The simulation case study allows assessing the performance of both designs with regard to cycle time reduction and robustness under parametric disturbances. Results evidence that the proposed model is detailed enough to provide accuracy in a simplified way, facilitating the implementation of in-line control strategies, which in turn enable significant reductions of drying time. [ABSTRACT FROM AUTHOR]

Purpose: Currently, in high‐dose rate (HDR) brachytherapy planning, the catheter's positions are often selected by the planner, which involves the planner's experience. The catheters are then inserted using a template that helps to guide the catheters. For certain applications, it is of interest to choose the optimal location and number of catheters needed for dose coverage and potential decrease of the treatment's toxicity. Hence, it is of great importance to develop patient‐specific algorithms for catheters and dose optimization. Methods: A modified Centroidal Voronoi tessellation (CVT) algorithm is implemented and merged with a graphics processing unit (GPU)‐based multi‐criteria optimization algorithm (gMCO). The CVT algorithm optimizes the catheters' positions, and the gMCO algorithm optimizes the dwell times and dwell positions. The CVT algorithm can be used simultaneously for insertion with or without a template. Some improvements to the CVT algorithm are presented such as a new way of considering the area that needs to be covered. One hundred eight previously treated prostates HDR cases using real‐time ultrasound are used to evaluate the different optimization procedures. The plan robustness is evaluated using two types of errors: deviations (random) in the insertion and deviation (systematic) in the reconstruction of the catheters. Results: Using gMCO on clinically inserted catheter increases the acceptance rate by 37% for Radiation Therapy Oncology Group (RTOG) criteria. Our results show that all the patients respect RTOG criteria with 11 catheters using CVT+gMCO with a template of 5 mm. The number of catheters needed for all patients to respect RTOG criteria with the freehand technique is 10 catheters using CVT+gMCO. When deviations are introduced, using a template, the acceptance rate goes to 85% with 3 mm deviations using 11 catheters. This decrease is less significant when the number of catheters is higher, decreasing by less than 5% with a 3 mm deviation using 13 catheters or more. In conclusion, it is feasible to decrease the number of catheters needed to treat most patients. Conclusions: Some cases still need a high number of catheters to reach the plan's criteria. Using gMCO allows an increase in the plan quality, while using CVT reduces the number of catheters. A higher number of catheters equates to plans that are more robust to deviations. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the impact of plant dynamics on the performance of steady-state data reconciliation for processes operating in stationary mode. Data reconciliation can effectively reduce effects of measurement errors on process variables estimation. This has greatly motivated the use of steady-state observers in the process industry. However, processes are operating under unsteady-state conditions and the fundamental hypothesis of steady-state is not respected. The objective of the paper is to develop criteria for assessing the impact of stationary disturbances on estimates quality and therefore to propose applicability indices for steady-state data reconciliation methods in disturbed plants. A generic criterion based on generalized variance is given and criteria are proposed for plant performance indicators using variance reduction in specific state-space directions. The various applicability indices are illustrated through a material balance example for a simulated mineral separation circuit and the steady-state approach is compared to an optimal stationary data reconciliation method. [Copyright &y& Elsevier]

Advanced process control can benefit to the operation efficiency and quality of tablets produced by wet granulation lines. Following the development of a new fully continuous fluidized bed reactor as a potential substitute for the current batch dryer design, this paper examines the energy and product quality performances of two predictive control strategies using an experimentally validated simulator. The first one proposes an economic model predictive controller explicitly minimizing the dryer energy consumption. The second one follows the same objective, but uses a simpler classical predictive control framework implementing targets on manipulated variables to drive them toward low energy consumption operating points. Both controllers lead to similar performances, and consume about 20% less energy than a reference feedback scheme without manipulated variable setpoints. Results also show that tracking the product quality attribute significantly benefits from a feedforward action because of the long residence time. This article addresses a subject little explored by the literature, the control of continuous fluidized bed dryers, and clearly demonstrates that a standard linear strategy can reduce the operating costs with an appropriate design. • Predictive control is applied on a simulated continuous fluidized bed dryer. • An economic model predictive controller (EMPC) minimizes the energy consumption. • A more standard predictive controller (MPC) also aims for low-energy actions. • Both EMPC and MPC results in low-energy operations. • Feedforward compensation improves the product quality in both cases. [ABSTRACT FROM AUTHOR]

• Novel control framework based on optimization in the latent space of a SMB-PLS model. • Real-time optimization of continuous process facing raw material fluctuation. • Overcomes parametric disturbances with model adaptation and closed-loop structure. • Adjustment of process operating conditions based on quality thresholds and economics. Reducing the impact of lot-to-lot raw material variability through optimization of operating conditions is key when the lots are already purchased, and available in inventory. The objective of this paper is to provide a framework to optimize operating conditions to maximize profitability while aiming at achieving product quality targets each time a new lot of raw material is fed to a continuous process. The proposed approach consists of solving an optimization problem in the latent space of a sequential multi-block partial least square model (SMB-PLS). Model updating and closed-loop operation are considered to overcome parametric disturbances. The approach is illustrated using a simulated grinding-flotation plant for a sequence of ore lots with variable properties. The case study shows that optimizing operating conditions with the proposed approach allows increasing biannual gain by 1.5 to 2 % compared to nominal operation. This represents between 59 and 75 % of the true achievable gain. [ABSTRACT FROM AUTHOR]

Decision tools to determine the acceptability of an incoming lot of raw materials prior to its purchase from a supplier is essential to ensure efficient quality control. They allow mitigating the propagation of the raw material variability through the process and, ultimately, in the final product quality. Establishing multivariate specifications regions for raw material properties to achieve high and consistent end product quality using latent variable models is key. However, these specifications are typically designed assuming the process operates at nominal conditions. The objective of this work is to enlarge the raw material acceptance region to accept materials having a wider range of properties, and possibly at a lower cost, while meeting final product quality requirements. Using a sequential multi-block partial least squares model (SMB-PLS), the design of multivariate specification regions is combined with optimization of process operating conditions to better cope with fluctuations in raw material properties. An economic criterion is used to assess whether accepting lower cost materials, at the expense of adjusting plant operation, which incurs additional costs, is economically justified. The approach is illustrated using a grinding-flotation plant simulator where the objective is to assess the profitability of different lots of raw materials (i.e. ores). The impact of measurement noise is also investigated. The case studies show that the raw material processability is improved since the number of correctly accepted lots is increased by 7.3 % in comparison with maintaining nominal process conditions. • Adjusting process conditions widens multivariate specifications for raw materials. • Optimization in the latent space of SMB-PLS model is performed for the first time. • Development of multivariate specifications includes an economic criterion. • A general SMB-PLS prediction equation is provided and demonstrated. [ABSTRACT FROM AUTHOR]

• Grey-box modeling is applied to a continuous horizontal fluidized bed dryer. • Product is displaced by a screw conveyor and fluidized by two gas chambers. • Data from a dynamically operated prototype validates the model. • Results highlight the shortcomings in the modeling assumptions. • Potential improvements to the prototype design are also raised. Fully continuous drying can improve the production flexibility and throughput of tablets manufactured by wet granulation lines. A recently developed prototype incorporates a screw conveyor to a horizontal fluidized bed dryer for pharmaceutical particles. Research on advanced process control strategies led to the publication of a two-phase tanks-in-series framework to describe its dynamics. This paper calibrates the model to replicate the prototype behavior. A grey-box identification methodology estimates the empirical and uncertain parameters using experimental input-output data. The underlying assumptions are cross-validated with time-domain comparisons, output root mean square errors and confidence intervals. Results show that the model reproduces the observed trends in general and the discrepancies result from mechanical issues on the equipment. [ABSTRACT FROM AUTHOR]

In the past years, a key improvement in the generation of treatment plans in high-dose-rate (HDR) brachytherapy comes from the development of multicriteria optimization (MCO) algorithms that generate thousands of pareto optimal plans within seconds. This brings a shift, from the objective of generating an acceptable plan to choosing the best plans out of thousands. Not only does the chosen plan depend on the planner, it also takes about 5-10 minutes to choose the preferred plan. The purpose of the present work is to speed up this process and to find a common ground for different specialists regarding the plan quality. An AI algorithm based on the ResNet deep neural network architecture is developed to choose the best plan(s) from the generated plans. The algorithm classifies the plans, from the 3D dose distribution and anatomical structures, in 3 different classes, (1) violating hard (minimum) criteria, (2) respecting hard criteria and (3) respecting soft criteria, with every class being more stringent than the last one (increase in plan quality). The three classes are based on dosimetric criteria used at our institution for 15 Gy in a single fraction. For the classification, the more confident the model is that a plan belongs to class 3, the better is the plan. To mimic the behaviour of experts, visual-like criteria are implemented for the bladder, rectum and urethra. Visual criteria are defined as 100% and 125% isodose distance from the organ at risk. During training, the algorithm learns the link between the inputs (3D dose and anatomy) and outputs (visual-like and DVH's criteria). 850 previously treated prostate's cancer patients are used for the training and another set of 20 patients previously evaluated by two experts (clinical medical physicist) as part of an inter-observer MCO study are used for validation. For the training, 100 plans are generated for each patient using MCO and 27 000 plans are chosen at random to have the same quantity in each class. A NVIDIA GeForce RTX 3090 is used for training. The model takes 20 s to classify 2000 plans in order of preference (vs 5-10 mins for experts to rank 4 preferred plans). Currently, the training time is not optimized and it takes less than 2 days to train on the 27 000 plans with 75 epochs. For the 20 validation patients, 39.9 ± 20.2%, 46.4 ± 15.3% and 14.5 ± 21.9% of the plan are in class 1, 2 and 3 respectively. Table 1 shows the results obtained on 20 cases, each with 2000 plans; the mean and deviation are calculated based on the plan chosen by the model and by the experts. The table includes the best ranked and worst ranked plan of class 3. Looking at the best plan according to the model and comparing it with the plan chosen by the two experts show that the behaviour is similar. Out of the 40 chosen plans by the two experts, on 3 occasions our model ranked the same plan as the best plan. Looking more in depth, we find that the median ranking of the plans chosen by expert 1 and 2 is 71.5 and 136.5 respectively out of 2000. In one of the cases, there is no plan respecting the DVH criteria of class 3 and the result is suboptimal; the plan chosen by each expert does not respect only 1 of the criteria while the plan chosen by our model does not respect 3 criteria. This type of behaviour is undesirable and one of the next steps is to address this rare problem, where it is unfeasible to reach all criteria. Adding visual criteria restricted the number of plans which were considered for class number 3 from 16 500 (originally) to 9 000. The approach is fast, adding negligible time to MCO planning, and preliminary results demonstrated the potential for clinical use. The approach is flexible with the possibility to adapt all criteria as desired. Future work will investigate model improvement, the non-inferiority of the best class 3 plan by the expert and methods to quickly restrict the number of navigated plans in order to obtain faster planning time. [ABSTRACT FROM AUTHOR]

• New dynamic macro scale model of a tablet pan coater based on energy and mass transfer equations. • Modeling for control system design and the development of system observers to estimate critical unmeasured variables in real-time. • Model parametrization and calibration using pilot plant batch data. • Model captures the main process variable trajectories, with errors below 2 °C for the outlet gas and tablet bed temperatures, and 2% for the outlet gas relative humidity. Aqueous film coating is a common and important step in the production of most pharmaceutical tablets. Controlling this process is beneficial to maintain stable and optimal operation and the use of a model capable of describing its main physical phenomena is fundamental for control system design. This paper presents the development of a dynamic macro scale model of a pan coater based on energy and mass transfer equations. Model complexity is kept low to simplify parameterization and allow for real-time applications, such as the development of system observers to estimate critical unmeasured variables. Pilot plant data from seven batches, ran according to designed experiments, are used for model calibration and validation. The final model adequately captures the global trajectories of the main process variables; mean differences between experimental and simulated values are below 2 °C for the outlet gas and tablet bed temperatures, and 2% for the outlet gas relative humidity. [ABSTRACT FROM AUTHOR]

The availability of reliable online moisture content measurements exploiting near-infrared (NIR) spectroscopy and chemometric tools allows the application of online control strategies to a wide range of drying processes in the pharmaceutical industry. In this paper, drying of particles with a pilot-scale batch fluidized bed dryer (FBD) is studied using a in-line NIR probe. A consolidated phenomenological state-space model of an FBD based on mass and energy balances is calibrated applying a nonlinear least-square identification to experimental data (grey-box modeling). Then, relying on the calibrated model, a nonlinear model predictive controller and a moving horizon state estimator are designed. The objective is to reach a specific particle moisture content setpoint at the end of the drying batch while decreasing cycle time and limiting particle temperature. A penalty term on the energy consumption can also be added to the usual tracking control cost function. Compared to a typical FBD operation in industry (mostly open-loop), it is shown that the drying time and the energy consumption can be efficiently managed on the pilot-scale process while limiting various operation problems like under drying, over drying, or particles overheating. [ABSTRACT FROM AUTHOR]

Purpose: Recently, our GPU-based multi-criteria optimization (gMCO) algorithm has been integrated in a graphical user interface (gMCO-GUI) that allows real-time plan navigation through a gMCO-generated set of Pareto-optimal plans for high-dose-rate (HDR) brachytherapy. This work reports on the commissioning of the gMCO algorithm into clinical workflow. Material and methods: Our MCO workflow was validated against Oncentra Prostate v. 4.2.2 (OcP) and Oncentra Brachy v. 4.6.0 (OcB). 40 HDR prostate brachytherapy patients (20 with OcP and 20 with OcB) were retrospectively replanned with gMCO algorithm by generating 2,000 Pareto-optimal plans. A single gMCO treatment plan was exported using gMCO-GUI plan navigation tools. The optimized dwell positions and dwell times of gMCO plans were exported via DICOM RTPLAN files to OcP/OcB, where final dosimetry was calculated. TG43 implementation in gMCO was validated against the consensus data of flexisource. Five analytical shapes were used as the ground truth for volume calculations. Dose-volume histogram (DVH) curves generated by gMCO were compared with the ones generated by OcP/OcB. 3D dose distributions (and isodose lines) were validated against OcP/OcB using dice similarity coefficient (DSC), 95% undirected Hausdorff distance (95% HD), and γ analysis. Results: Differences between -0.4% and 0.3% were observed between gMCO calculated dose rates and the flexisource consensus data. gMCO volumes were within ±2% agreement in 3/5 volumes (deviations within -2.9% and 0.1%). For 9 key DVH indices, the differences between gMCO and OcP/OcB were within ±1.2%. Regarding the accuracy of key isodose lines, the mean DSC was greater than 0.98, and the mean 95% HD was below 0.4 mm. The fraction of voxels with γ ≤ 1 was greater than 99% for all cases with 1%/1 mm threshold. Conclusions: The GPU-based MCO workflow was successfully integrated into the clinical workflow and validated against OcP and OcB. [ABSTRACT FROM AUTHOR]

• Individual anode current signals used to detect anode incidents in real-time. • Detection systems built with principal component analysis and an industrial dataset. • Best detection performance was achieved with the simplest system configuration. • Model profit metric developed to evaluate economic potential of detection systems. • Detection systems appear to be economically superior to standard detection method. Anodic incidents occur when an aluminum electrolysis cell short circuits at an anode position. They reduce the current efficiency of the affected cell, which makes it economically advantageous to detect and remove them as early as possible. This study evaluates the performance of three anodic incident detection systems of varying complexity. Individual anode current signals from 6 different cells were monitored with a Principal Component Analysis (PCA) model to detect anodic incidents using the Squared Prediction Error (SPE) statistic. The results suggest that a simple detection system using a common PCA model and a common detection threshold for all cells may be preferable. A novel approach to estimating the economic benefit of a detection system was also developed and used to optimize the systems' detection threshold. The anodic incident detection systems were found to deliver positive profits relative to the standard detection technique. [ABSTRACT FROM AUTHOR]

Abstract: In mineral or metallurgical processing plants, it is a common practice to analyze physical properties or chemical content of the flowing material. However, only a few strategic flowrates, such as global plant or specific process unit feed flowrates, are usually measured. Other flowrates must then be estimated using data reconciliation or suboptimal direct estimation methods. Despite the optimality of data reconciliation, direct methods are still widely used in the industry because of their simplicity. Typical applications of suboptimal methods are to provide reliable estimates for data reconciliation algorithm initialization, support the selection of components to be analyzed, and to detect faults prior mass balancing calculations. Falling into the category of suboptimal methods are the minimal estimability (generalization of the classical n-product formula), ordinary regression, and node imbalance methods. The objectives of the paper are to review these methods under a unified approach, to compare their performance in terms of estimated flowrate variance, and to propose a systematic method to design measurement strategies maximizing the performance of flowrates estimation. The concepts are illustrated using both simulated and real plant data from the mineral processing industry. [Copyright &y& Elsevier]

[Display omitted] In this study, an in-line Process Analytical Technology (PAT) for cosmetic (non-functional) coating unit operations is developed using images of the tablet bed acquired in real-time by an inexpensive industrial camera and lighting system. The cosmetic end-point of multiple batches, run under different operating conditions, is automatically computed from these images using a Multivariate Image Analysis (MIA) methodology in conjunction with a stability determination strategy. The end-points detected by the algorithm differed, on average, by 3% in terms of total batch time from those identified visually by a trained operator. Since traditional practice typically relies on a coating overage to ensure full batch aspect homogeneity in the face of disturbances, the current in-line method can be used to reduce coating material and processing time (over 40% for the operating policy adopted in this work). Additionally, monitoring of the color features calculated by the algorithm allowed the identification of abnormal process conditions affecting visible coating uniformity. This work also addresses practical challenges related to image acquisition in the harsh environment of a pan coater, bringing this tool closer to a state of maturity for implementation in production units and opening the path for their optimization, monitoring, and automatic control. [ABSTRACT FROM AUTHOR]

The cooling zone of an induration furnace exhibits a nonlinear dynamic behavior in addition to a strong coupling between output pressure and temperature. Simulation studies show that linear controller performance is unacceptable from an industrial point of view. In order to obtain adequate performance on a wide operating range, a nonlinear predictive controller (NLMPC) based on a phenomenological process model is proposed. Since the furnace simulation model shows that the equipment behaves as a Hammerstein model, a variable change is performed and a linear model predictive controller (MPC) is developed for the cooling zone. Both controllers are tested for set-point changes and disturbance rejection and give relatively similar performances. It is concluded that for processes having structured nonlinearities, as the cooling zone considered here, linear MPC should be preferred to NLMPC since the computation time is far less demanding and the industrial implementation easier. [ABSTRACT FROM AUTHOR]

Batch fluidized bed drying is a common process for water removal in pharmaceutical particles. Multiple phenomenological models for this type of dryer are proposed in the literature, with many of them relying on the two-phase theory. This article elaborates on a two-phase model and extends it for the pharmaceutical case by improving the particle, batch size and heat loss descriptions. An explicit methodology with low computational cost is also developed for solving the fluidized bed equations. Then, based on a grey-box modeling strategy, parameters with known value are fixed, while the unknown parameters are calibrated with experimental data collected on a pilot scale fluidized bed dryer. Lastly, the model is dynamically validated on distinct experimentation datasets. It is shown that the simulations are able to reproduce the pilot data trends, but there is still room for improvements in the heat loss representation. [ABSTRACT FROM AUTHOR]

Background: Surgery is increasingly reserved for complicated diverticulitis. The role of laparoscopy in this context is ill defined. This study aimed to evaluate the safety, feasibility, and outcomes associated with the application of laparoscopy to an unrestricted spectrum of diverticular pathologies, with an emphasis on complicated disease. Methods: Consecutive patients who underwent elective, urgent, or emergent laparoscopic colectomy for diverticular disease from 1991 to 2007 were analyzed from a prospectively collected database. Laparoscopy was offered to all patients presenting for surgical attention, thus minimizing selection bias. Complicated cases had abscesses, perforations, fistulas, or strictures. Uncomplicated cases had chronic or recurrent diverticulitis. Summary statistics and univariate comparisons were generated. Results: A total of 183 patients were analyzed, including 39 complicated cases. The complicated cohort included 12 abscesses or perforations (31%), 18 fistulas (46%), and 11 strictures (28%). Intraoperative complications were comparable between the two groups (7.7 vs. 9.7%), although the complicated cases resulted in more conversions (23 vs. 4.2%; p = 0.0007). More than 79% of the complicated patients and 96% of the uncomplicated patients underwent unprotected primary anastomosis. Medical (23 vs. 1.4%; p < 0.0001) and surgical (28 vs. 14%; p = 0.035) complications were more frequent in the complicated group. Leak rates were acceptably low (6.5 vs. 2.2%; p = 0.23). There were no recorded deaths. Finally, the time until discharge from hospital was significantly longer in the complicated group by a median of 1 day. Conclusions: The laparoscopic management of complicated diverticular disease is feasible and appears to be safe in the hands of experts. Despite a high rate of conversion to open surgery, laparoscopy was the sole operative intervention for the majority of patients with complicated diverticular disease. Further studies are needed to allow rigorous comparison with an open control group. [ABSTRACT FROM AUTHOR]

Background The recent introduction of hand-assist devices in laparoscopic colorectal surgery has renewed interest in the influence of incision length. This study aimed to define the impact of extraction incision length on the postoperative outcomes of laparoscopic left-sided colon and rectal resections. Methods Consecutive patients undergoing laparoscopic left-sided colorectal resection from 1991 to 2007 were retrieved from a prospectively collected database. The association between incision length and patient characteristics, diagnosis, and perioperative outcomes were analyzed using logistic regression, Spearman correlation, Wilcoxon test, and chi-square test. Results A total of 494 laparoscopic colorectal resections (left, sigmoid, anterior, and low anterior resections) were retrieved. Patients with conversions to open surgery (n = 59) and missing data (n = 53) were excluded. As a result, 382 cases were included in the study. A slight majority of the patients had malignant disease (n = 202, 53%). The median incision length was 5 cm (interquartile range, 4-6 cm). Increasing weight was positively correlated with incision length (p = 0.0001). Male patients had modestly larger mean incisions than female patients (5.5 vs. 5.0 cm; p = 0.0075). Age, previous surgery, diagnosis, days to resumption of normal diet, and days to discharge from hospital showed no significant relationship with incision length. No association was observed between the incision length and intraoperative or postoperative complications. Conclusions Patients undergoing laparoscopic colorectal surgery appear to achieve the same perioperative outcomes irrespective of their extraction incision lengths. To maintain the short-term benefits of laparoscopy, surgeons should consider pursuing a minimally invasive technique, even when a larger extraction incision will ultimately be required. [ABSTRACT FROM AUTHOR]

This study aimed to define the management and risk factors for intraoperative complications (IOC) and conversion in laparoscopic colorectal surgery, and to assess whether surgeon experience influences intraoperative outcomes. Consecutive patients undergoing laparoscopic colorectal procedures from 1991 to 2005 were analyzed from a longitudinal prospectively collected database. All patients referred to the four surgeons involved in this study were offered a minimally invasive approach. Patient characteristics, perioperative variables, and surgeon experience data were analyzed and compared. A total of 991 consecutive laparoscopic colorectal procedures were studied. The majority of operations were performed for malignant disease ( n = 526, 53%), and most frequently consisted of segmental colonic resections ( n = 718, 72%). A total of 85 patients (8.6%) had an IOC. Patients experiencing an IOC had a significantly higher median body weight (75 versus 68 kg, p = 0.0047) and had a higher proportion of previous abdominal surgery (31% versus 20%, p = 0.029). Only 39% of patients suffering an IOC required conversion to open surgery. A total of 126 (13%) cases were converted to open surgery. On multivariable analysis, previous abdominal surgery [odds ratio (OR) 3.40, 95% confidence interval (CI) 1.39–8.35, p = 0.0076] was independently associated with having an IOC and a conversion to open within the same procedure. With increasing experience, individual surgeons were found to operate on heavier patients ( p = 0.025), and on patients who had a higher rate of previous intra-abdominal surgery ( p < 0.0001). Despite these risk factors, the early and late experience demonstrated no significant difference in terms of IOCs ( p = 0.54) and conversion to open surgery ( p = 0.40). The majority of IOCs can be managed laparoscopically. With increasing experience surgeons can perform laparoscopic colorectal surgery on a patient population with a greater proportion of previous abdominal surgery and a higher mean body weight without adversely affecting their rates of intraoperative complications or conversion. [ABSTRACT FROM AUTHOR]

The cooling zone of an induration furnace is a highly interactive multivariable process with strong nonlinearities and dissimilar dynamics. Linear controllers, implemented on a first-principles process model, are unable to properly control the unit in a wide operating range. This paper proposes a design procedure which considers relevant process characteristics, such as nonlinearity, interaction, directionality, and dynamics, for the synthesis of decentralised extended PIDs and model-based predictive controllers (MPCs). Linear controllers with variable transformations are used since the process model shows that a Hammerstein model can approximate the process nonlinear behaviour. The decentralised PIDs are tuned using efficient rules that take into account the process interaction. The performance of both control strategies is evaluated for set-point tracking, disturbance rejection, and robustness to modelling errors. Similar results are obtained for the gas temperature control, which is the most important process variable. Slightly better results are obtained with the MPC for the gas pressure, the fastest dynamic variable. [Copyright &y& Elsevier]

• A dynamic model of a continuous horizontal fluidized bed dryer is developed. • A screw conveyor control the product residence time in the dryer. • The model allows for gas bubbles and product axial mixing. • Parameters are lumped and equations are solved at low computational costs. • Simulation results are consistent with theory. The bottleneck of pharmaceutical continuous wet granulation lines may often be the drying stage. The introduction of horizontal fluidized beds is currently contemplated to solve this issue, inter alia. However, it would require an appropriate process control system to reach and maintain over time the targeted residual moisture of the product. To support the endeavor of both the process and control designs, this paper develops a dynamic model with lumped parameters of a continuous horizontal fluidized bed dryer equipped with a screw conveyor. To this end, the framework makes use of a two-phase tanks-in-series configuration, thus taking into account expected gas bypasses (bubbles) and product axial mixing. Array programming and vectorized operations on each tank reactor allow solving equations explicitly at a low computational cost. Simulation results for calcium carbonate granules in a laboratory scale dryer are consistent with fluidization, drying and continuous reactor fundamentals. [ABSTRACT FROM AUTHOR]