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In this work, an Android application for measurement of nitrite concentration and pH determination in combination with a low-cost paper-based microfluidic device is presented. The application uses seven sensing areas, containing the corresponding immobilized reagents, to produce selective color changes when a sample solution is placed in the sampling area. Under controlled conditions of light, using the flash of the smartphone as a light source, the image captured with the built-in camera is processed using a customized algorithm for multidetection of the colored sensing areas. The developed image-processing allows reducing the influence of the light source and the positioning of the microfluidic device in the picture. Then, the H (hue) and S (saturation) coordinates of the HSV color space are extracted and related to pH and nitrite concentration, respectively. A complete characterization of the sensing elements has been carried out as well as a full description of the image analysis for detection. The results show good use of a mobile phone as an analytical instrument. For the pH, the resolution obtained is 0.04 units of pH, 0.09 of accuracy, and a mean squared error of 0.167. With regard to nitrite, 0.51% at 4.0 mg L-1 of resolution and 0.52 mg L-1 as the limit of detection was achieved. [ABSTRACT FROM AUTHOR]

This study focuses on the applications of model checkingtechniquesin automatic synthesis for the reachability of process systems. Modelchecking is an automatic method used to verify if a circuit or a condition,expressed as a concurrent transition system, satisfies a set of properties.The strength of this method lies in its ability to synthesize a feasiblesequence with a counterexample and to verify its correctness usingtemporal logics such as computation tree logic (CTL) simultaneously.These challengeable approaches are implemented in a commercial softwarepackage (UPPAAL) using graphical discrete modeling, automata theory,and CTL to automate the synthesis for the reachability of processsystems. Three use cases of interest in computer-aided engineeringdue to the difficulty involved in synthesizing them manually are explored.In the first use case, the optimal operating procedure according tothe objective function of a paper mill process is synthesized. Inthe second use case, a challengeable attempt to optimize supply chainnetworks is tested. In the third use case, alternative pathways withthe desired constraints are found in biochemical networks. The paperalso explains how the proposed model checking method can be used efficientlyin graphical modeling with automata theory to automatically synthesizeprocess systems counterexamples using CTL by means of the case studies. [ABSTRACT FROM AUTHOR]

Soft sensors are computer programs and inferential estimators with an auxiliary role in predicting the variables that are impossible or difficult to obtain by available process measurements. This paper presents a soft sensor design using the Fuzzy C-Means clustering combined with the Recursive Finite Newton algorithm to train a nonlinear support vector regression (FCM-RFN-SVR) machine. In the proposed approach, samples are split into some partitions; then, by means of RFN-SVR, a local model for each partition is set. The soft sensor presented in this paper is designed to be implemented on a distillation column so that it can predict the bottom product benzene concentration. Compared to typical support vector regression (SVR), the results demonstrate that the proposed method is more powerful in improving the generalization ability of the soft sensor (smaller mean squared error (MSE) and larger coefficient of determination (R²) for the testing data). [ABSTRACT FROM AUTHOR]

Reactivegranulation is a complex process which brings about a physical andchemical change in the raw materials. It is extensively used for theproduction of detergents through the “dry neutralization”reaction. In this paper, the dynamics of the reactive granulationprocess is captured using a coupled multidimensional population balancemodel (PBM) for the size enlargement and a diffusion-reaction (DR)equation for the kinetics of the reaction. This framework can capturethe experimental observations that have also been reported in Schöngutet al. (Ind. Eng. Chem. Res.2011, 50, 11576). There are a few empirical parameters involvedin the modeling framework for which a dynamic sensitivity analysiswas employed to shortlist the parametric quantities that have thelargest influence on the critical quality attributes. The model iscalibrated against experimental results via the Nelder–Meadsimplex algorithm for estimating the empirical parameters, and themodel predictions were compared with experimental results. Both theparameter estimation and prediction results showed good agreementwith the experimental results. The development in terms of a comprehensivemathematical model and the results as presented in this paper suggestthe ability of this approach to make suitable model predictions whichcan significantly reduce the number of experimental trials requiredfor process optimization and control. [ABSTRACT FROM AUTHOR]

Flexibilityand stability are key components of chemical processoperability and are important in practical applications. In previousresearch, flexibility and stability have been studied separately,resulting in flexible regions containing unstable parts that shouldbe avoided in terms of operability. This paper considers flexibilityand stability simultaneously, with the aim of obtaining a stable flexibleregion for the process. On the basis of existing algorithms for flexibilityanalysis, the vertex algorithm and active constraint algorithm arepresented for the simultaneous analysis of flexibility and stability.Certain limitations of these algorithms are explored and discussed.Then, using the eigenvalue optimization method, we propose a new formulationof the simultaneous analysis of process flexibility and stability.Case studies of the methyl methacrylate polymerization and 1,3-propanediolfermentation are provided. The obtained results illustrate the needto consider flexibility and stability simultaneously and demonstratethe effectiveness of the novel method presented in this paper. [ABSTRACT FROM AUTHOR]

In this work the control allocation (CA) philosophy is considered in order to propose a plantwide control (PWC) design methodology. For medium/large-scale processes for which the number of actuators is greater than the number of controlled variables, a CA-based strategy can be used to distribute the total control effort among the actuators, with some attractive features. First, the CA module can explicitly handle (i) constraints on inputs (typically actuator position and rate limits) and (ii) additional control objectives (e.g., to penalize the actuators control energy). Second, CA has the potential to provide (actuator) fault tolerance without considering a control structure reconfiguration. In this paper, a decentralized control structure is proposed for computing the total control effort, which is implemented with conventional single-input single-output PID controllers. In addition, a CA module is configured and two alternative algorithms are compared, namely, (i) generalized inverse plus simple saturation and (ii) active set method for a weighted least-squares formulation. The complete structure can be designed based on steady-state process information. The well-known Tennessee Eastman case study is considered to evaluate the proposal. Several closed-loop simulations are presented to show the dynamic performance of the PWC structure and the timing properties of the CA algorithm. [ABSTRACT FROM AUTHOR]

The problem of value-optimal sensor network design for linear systems has been shown to be of the nonconvex mixed integer programming class. While the branch and bound search procedure can be used to obtain a global solution, such a method is limited to fairly small systems. The bottleneck is that during each iteration of the branch and bound search, a fairly slow semi-definite programming (SDP) problem must be solved to its global optimum. In this paper, it is demonstrated that an equivalent reformulation of the nonconvex mixed integer programming problem and subsequent application of the generalized benders decomposition (GBD) algorithm will result in massive reductions in computational effort. While the proposed algorithm has to solve multiple mixed integer linear programs, this increase in computational effort is significantly outweighed by a reduction in the number of SDP problems that must be solved. [ABSTRACT FROM AUTHOR]

The process industry is moving toward rigorous flowsheet design optimization with modern algorithms. Optimization results are, however, influenced by uncertainty in the parameters of the mathematical models used in the optimization calculation. Parametric uncertainty is typically addressed using scenario-based approaches, whereby the process is optimized for a predetermined, finite set of scenarios representing the statistical properties of the parameters. This paper presents a novel approach for process design under uncertainty. The framework exploits the semi-infinite nature of sequential dynamic optimization, and is based on representing the uncertain parameters as continuous, time-varying disturbance variables acting on a (static) process model over a pseudo-time domain. The parameter uncertainty space is mapped by intersecting, continuous parameter trajectories instead of a limited set of discrete scenarios. We test the proposed strategy on two case studies: a dimethyl ether plant and the Williams-Otto process, demonstrating superior computational performance compared to scenario-based approaches. [ABSTRACT FROM AUTHOR]

Historical normal and abnormal data sets are prerequisites for process monitoring, alarm system rationalization, fault detection, and diagnosis. This paper proposes a new method to automatically find normal and abnormal data segments from historical data sets based on variational directions of multiple process variables. The minimum time duration and the minimum amplitude shift are introduced as empirical knowledge to define underlying stages in the data sets. Two major challenges in identifying these stages are addressed by using a density-based clustering algorithm. The effectiveness of the proposed method is illustrated using numerical and industrial examples. [ABSTRACT FROM AUTHOR]

The change point detection method (Watkins, L. P.; Yang, H. J. Phys. Chem. B 2005, 109, 617) allows the objective identification and isolation of abrupt changes along a data series. Because this method is grounded in statistical tests, it is particularly powerful for probing complex and noisy signals without artificially imposing a kinetics model. The original algorithm, however, has a time complexity of O N 2), where N is the size of the data and is, therefore, limited in its scalability. This paper puts forth a parallelization of change point detection to address these time and memory constraints. This parallelization method was evaluated by applying it to changes in the mean of Gaussian-distributed data and found that time decreases superlinearly with respect to the number of processes (i.e., parallelization with two processes takes less than half of the time of one process). Moreover, there was minimal reduction in detection power. These results suggest that our parallelization algorithm is a viable scheme that can be implemented for other change point detection methods. [ABSTRACT FROM AUTHOR]

In this paper, a biologically inspired optimal control strategy, denoted as BIO-CS, is proposed for nonlinear chemical processes with constraints. This approach combines the ants' rule of pursuit idea with multiagent and optimal control concepts. In this agent-based framework, starting from an initially feasible trajectory for the leader agent, each follower agent improves its path toward the set point by employing optimal control laws. As the number of agents progresses, the trajectories converge to an optimal solution. The developed algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower local interactions. The effectiveness of the developed approach is illustrated by addressing the nonlinear dynamic model of a fermentation process to produce ethanol, whose challenges in process dynamics include oscillations and steady-state multiplicity. The proposed method is successfully implemented for this system considering set point tracking, disturbance rejection, and plant-model mismatch scenarios. From the simulation results, the generated solutions for the agents show error reduction, computed as integrated time absolute error, with respect to the set point as the agents' trajectories converge to the optimal solution. The performance of the proposed approach is also compared to that of a classical proportional-integral controller and a single optimal control solver implementation. This comparison indicates the potential of the proposed BIO-CS algorithm for improved performance, computational time reduction, and faster offset-free set point tracking. [ABSTRACT FROM AUTHOR]

The feed heating furnace is an important devise in many petrochemical processes. Keeping under control the temperature of the feeding improves the performance and quality of the final product. Industrial furnaces usually have multiple burners and parallel tube-passes to handle the throughputs and reduce the furnace pipe pressure. In this paper, a design method based on distributed deviation for load balancing and temperature tracking control in the set of passes is proposed. The control actions on the inlet feed and fuel flow rates are included in the constraint conditions of the controller design algorithm. More practically, the temperature differences among passes are guaranteed in a boundary range over a period of time by relaxing the requirement of asymptotic consensus of temperature control across all passes in steady-state. The control scheme shows to be more cost-effective when there is a large number of passes. The simulation results illustrate that the new controller design method is effective and the distributed deviation control scheme is feasible for a class of industrial feed heaters. [ABSTRACT FROM AUTHOR]

White light interference is used as a label-free method to detect nanoscale changes on surfaces. However, the signal-to-noise ratio of the white light interference method is very low, thus resulting in inaccurate results. In this paper, we report a corrected label-free method based on hyperspectral interferometry to overcome the shortcoming of the white light interference method. A platform based on hyperspectral interferometry was established, and a DNA hybridization microarray was constructed to quantitate thickness variation of molecules on a solid surface. We used fluorescence resonance energy transfer (FRET) to validate the results of our method. Compared to conventional fluorescence-labeled method like FRET, our method has advantages because it does not require a fluorescent label and has a detection limit of 1.78 nm, a high accuracy, and wide detection range (5-64 bp). [ABSTRACT FROM AUTHOR]

Energy decomposition analysis (EDA) is a widely used tool for extracting physical and chemical insights from electronic structure calculations of intermolecular interactions, as well as for the development of advanced force fields for describing those interactions. Recently, the absolutely localized molecular orbital (ALMO) EDA has been extended from the self-consistent field level to the second-order Møller--Plesset (MP2) theory level. This paper reports an efficient implementation of the MP2 ALMO-EDA that scales optimally, employs the resolution of the identity (RI) approximation for post-SCF matrix elements, and is shared-memory parallel. The algorithms necessary to achieve this implementation are described in detail. Performance tests using the aug-cc-pVTZ basis set for water clusters of up to 10 molecules are reported. The timings suggest that the MP2 ALMO-EDA is computationally feasible whenever MP2 energy calculations themselves are feasible, and the cost is dominated by the SCF itself in this size regime. The MP2 ALMO-EDA is applied to study the origin of substituent effects in anion-π interactions between chloride and benzene and mono- through hexafluorobenzene. The effect of fluoro substituents was primarily to change the frozen interaction. Detailed analysis supports the interpretation that anion-π interactions are favorable because of electrostatic interaction with the substituents. [ABSTRACT FROM AUTHOR]

Multiphase characteristics and uneven-length batch duration have been two critical issues to be addressed for batch process monitoring. To handle these issues, a variable moving window-k nearest neighbor (VMW-kNN) based local modeling, irregular phase division, and monitoring strategy is proposed for uneven batch processes in the present paper. First, a pseudo time-slice is constructed for each sample by searching samples that are closely similar to the concerned sample in which the variable moving window (VMW) strategy is adopted to vary the searching range and the k nearest neighbor (kNN) rule is used to find the similar samples. Second, a novel automatic sequential phase division procedure is proposed by similarity evaluation for local models derived from pseudo time-slices to get different irregular phases and ensure their time sequence. Third, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. The proposed strategy can be readily extended to the case with limited batches. To illustrate the feasibility and effectiveness, the proposed algorithm is applied to a typical multiphase batch process, i.e., injection molding process, with an uneveness problem. [ABSTRACT FROM AUTHOR]

Here, a nonlinear plant is considered, which is operated by a decentralized control system. The existing system ignores the interactions between subsystems, which often results in uncaptured plantwide performance. The focus of this paper is on the design of a distributed model predictive control (DMPC) network using successively linearized internal models. In this method, all existing interactions between the subsystems should be considered in order to enhance the performance of the current decentralized DMPC scheme. A coordination layer is added to the existing network, while minor modifications are applied to the local MPC controllers, to achieve the performance and stability of a hypothetical centralized MPC for the entire plant. In this work, an interior-point algorithm is proposed to coordinate a DMPC network via the price-driven coordination approach. In addition, the convergence of the algorithm is shown, and the necessary conditions to ensure the closed-loop stability of the system are provided for the situation when the algorithm is terminated prematurely prior to convergence. [ABSTRACT FROM AUTHOR]

In Part 1 of this paper, we presented a scenario-basedmultiperiodmixed-integer linear programming (MILP) formulation for a capacityplanning problem in continuous pharmaceutical manufacturing underclinical trials uncertainty. The number of scenarios and, thus, theformulation size grows exponentially with the number of products.The model size easily becomes intractable for conventional algorithmsfor more than 8 products. However, industrial-scale problems ofteninvolve 10 or more products, and thus a scalable solution algorithmis essential to solve such large-scale problems in reasonable times.In this part of the paper, we develop a rigorous decomposition strategythat exploits the underlying problem structure. We demonstrate theeffectiveness of the proposed algorithm using several examples containingup to 16 potential products and over 65 000 scenarios. Withthe proposed decomposition algorithm, the solution time scales linearlywith the number of scenarios, whereby a 16-product example with over65 million binary variables, nearly 240 million continuous variables,and over 250 million constraints was solved in less than 6 h ofsolver time. [ABSTRACT FROM AUTHOR]

In the development of soft sensors for an industrial process, the colinearity of the predictor variables and the time-varying nature of the process need to be addressed. In many industrial applications, the partial least-squares (PLS) has been proven to capture the linear relationship between input and output variables for a local operating region; therefore, the PLS model needs to be adapted to accommodate the time-varying nature of the process. In this paper, a fast moving window algorithm is derived to update the PLS model. The proposed approach adapted the parameters of the inferential model with the dissimilarities between the new and oldest data and incorporated them into the kernel algorithm for the PLS. The computational loading of the model adaptation was therefore independent of the window size. In addition, the prediction performance of the model is only dependent on the retained latent variables (LVs) and the window size that can be predetermined from the historical data. Since a moving window approach is sensitive to outliers, the confidence intervals for the primary variables were created based on the prediction uncertainty. The inferential model would not be misled by the outliers from the online analyzers, whereas the model could be updated during the transition stage. The prediction performance of a soft sensor is not only dependent on the capability of the inferential model, but also relies on the data quality of the input measurements. In this paper, the input sensors were validated before performing a prediction. The deterioration of the prediction performance due to the failed sensors was removed by the reconstruction approach. A simulated example of a continuous stirred tank reactor (CSTR) with feedback control systems illustrated that the process characteristics captured by the PLS could be adapted to accommodate a nonlinear process. An industrial example, predicting oxygen concentrations in the air separation process, demonstrated the effectiveness of the proposed approach for the process industry. [ABSTRACT FROM AUTHOR]

Prediction of protein folding rates and folding nuclei is an important problem of protein science. Most of the previously proposed models for protein folding in vitro are based on the nucleation mechanism of this process. Our model considering protein folding as a flow arising in a network of folding−unfolding pathways at a coarse-grained free-energy landscape was described a few years ago, along with an algorithm for calculation of protein folding rates. Here we extend our approach and describe in detail a mathematically strict algorithm for calculating the “folding nuclei”, arising as bottlenecks of the flow. Although the proposed physical theory uses no adjustable parameters, its results are in good agreement with experiment. This paper presents (i) the general theory and (ii) the results for the simplest case, i.e., folding/unfolding at the midpoint of thermodynamic equilibrium between the native and unfolded states of a protein; results for “in-water” conditions, i.e., for the case when no denaturant is added and the native state of a protein is much more stable than the unfolded one, will be described in the next paper of the series. [ABSTRACT FROM AUTHOR]

Complex tensor contraction expressions arise in accurate electronic structure models in quantum chemistry, such as the coupled cluster method. This paper addresses two complementary aspects of performance optimization of such tensor contraction expressions. Transformations using algebraic properties of commutativity and associativity can be used to significantly decrease the number of arithmetic operations required for evaluation of these expressions. The identification of common subexpressions among a set of tensor contraction expressions can result in a reduction of the total number of operations required to evaluate the tensor contractions. The first part of the paper describes an effective algorithm for operation minimization with common subexpression identification and demonstrates its effectiveness on tensor contraction expressions for coupled cluster equations. The second part of the paper highlights the importance of data layout transformation in the optimization of tensor contraction computations on modern processors. A number of considerations, such as minimization of cache misses and utilization of multimedia vector instructions, are discussed. A library for efficient index permutation of multidimensional tensors is described, and experimental performance data is provided that demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

The main challenge in developing soft sensors in process industry is the existence of irregularity of data, such as measurement noises, outliers, and missing data. This paper is concerned with a comparative study among various data-driven soft sensor algorithms and the Bayesian methods. The algorithms to be considered for a comparative study in this paper include ordinary least-squares, robust regression, error-in-variable methods, partial least-squares, and the Bayesian inference algorithms. Methods for handling irregular data are reviewed. An iterative Bayesian algorithm for handling measurement noise and outliers is proposed. Performance of the Bayesian methods is compared with other existing methods through simulations, a pilot-scale experiment, and an industrial application. [ABSTRACT FROM AUTHOR]

In this paper, a general algorithm is developed to address the multiparametric mixed-integer linear programming (mpMILP) problem with uncertain parameters in the left-hand side (LHS), right-hand side (RHS), and objective function coefficients simultaneously. The algorithm is based on a general multiparametric linear programming (mpLP) algorithm, which is derived using the optimality conditions of standard linear programming (LP) problem. The intent of the proposed framework is to propose a general framework to address different uncertainties in the process engineering problems. In addition, the paper also discusses the solution of the special problem where LHS uncertainty is included in the optimization model as a coefficient of continuous variable, and it notes the high computational complexity needed to retrieve the rigorous solution of large-scale problems, because of the nonlinearities of the objective function and nonconvex critical regions. Several numerical examples are presented to illustrate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

A procedure for the design of a sensor network is reported in this paper. The strategy uses the graph theory and takes the reliability objective and cost constraint into account. According to the requirements of industrial application, the approach presented in this paper can obtain an efficient solution even though there are not enough sensors to guarantee the whole system's observability. The proposed algorithm can also provide a solution in the situation where some important variables have to be measured and a portion of variables cannot be directly measured because of technical reasons. Industrial examples are provided to show the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

This paper describes an adaptive algorithm for interpolation over a library of molecules subjected to synthesis and property assaying. Starting with a coarse sampling of the library compounds, the algorithm finds the optimal substituent orderings on all of the functionalized scaffold sites to allow for accurate property interpolation over all remaining compounds in the full library space. A previous paper introduced the concept of substituent reordering and a smoothness-based criterion to search for optimal orderings (Shenvi, N.; Geremia, J. M.; Rabitz, H. J. Phys. Chem. A 2003, 107, 2066). Here, we propose a data-driven root-mean-squared (RMS) criteria and a combined RMS/smoothness criteria as alternative methods for the discovery of optimal substituent orderings. Error propagation from the property measurements of the sampled compounds is determined to provide confidence intervals on the interpolated molecular property values, and a substituent rescaling technique is introduced to manage poorly designed/sampled libraries. Finally, various factors are explored that can influence the applicability and interpolation quality of the algorithm. An adaptive methodology is proposed to iteratively and efficiently use laboratory experiments to optimize these algorithmic factors, so that the accuracy of property predictions is maximized. The enhanced algorithm is tested on copolymer and transition metal complex libraries, and the results demonstrate the capability of the algorithm to accurately interpolate various properties of both molecular libraries. [ABSTRACT FROM AUTHOR]

This paper addresses the need for surveillance of fugitive methane emissions over broad geographical regions. Most existing techniques suffer from being either extensive (but qualitative) or quantitative (but intensive with poor scalability). A total of two novel advancements are made here. First, a recursive Bayesian method is presented for probabilistically characterizing fugitive point-sources from mobile sensor data. This approach is made possible by a new cross-plume integrated dispersion formulation that overcomes much of the need for time-averaging concentration data. The method is tested here against a limited data set of controlled methane release and shown to perform well. We then present an information-theoretic approach to plan the paths of the sensorequipped vehicle, where the path is chosen so as to maximize expected reduction in integrated target source rate uncertainty in the region, subject to given starting and ending positions and prevailing meteorological conditions. The information-driven sensor path planning algorithm is tested and shown to provide robust results across a wide range of conditions. An overall system concept is presented for optionally piggybacking of these techniques onto normal industry maintenance operations using sensorequipped work trucks. [ABSTRACT FROM AUTHOR]

In industrial applications, many batch/repetitive systems are constructed by two or more subprocesses connected in serial to perform a given task repetitively or periodically, termed as cascaded batch/repetitive processes. In this paper, by introducing a two-dimensional (2D) cost function, an integrated design method of a novel cascade control scheme is developed for this kind of process under the 2D model predictive iterative learning control infrastructure. The resulted cascade control scheme essentially consists of a real-time cascade model predictive control along the time direction as the inner-loop control and an iterative learning control (ILC) along the cycle direction as the outer-loop control. The proposed design algorithm not only realizes an integrated design for all control loops but also guarantees optimal control in terms of the 2D control performance. The convergence criterion of the closed-loop control system along the cycle index is also investigated. Compared to the conventional cascade control schemes, the proposed algorithm is essentially a 2D feedback control with superior control performances which are clearly illustrated by the simulation results on the injection molding process. [ABSTRACT FROM AUTHOR]

Usually, industrial processes have multiple operational modes due to different production strategies, external environmental variability, or changes in product specifications. Monitoring of multimode processes constitutes a challenging problem considering multiple steady-state operational regions and dynamic transitions. This paper proposes a novel method for the offline identification of stable modes and transitions based on a local kernel density estimation algorithm. The online monitoring scheme is based on mode identification and transition tracking. The Tennessee Eastman (TE) benchmark process is used as a case study to evaluate the performance of the proposal. As a result, stable modes are successfully isolated from transitions, even when these involve complex changes in the production mode. The results also demonstrate that the proposed scheme is capable of tracking mode changes, and finally, results monitored during transitions confirm the validity and efficacy of the new approach compared with previous works. [ABSTRACT FROM AUTHOR]

Oil refining is one of the most complex activities in the chemical industry due to its nonlinear nature, which ruins the convexity properties and prevents any guarantees of the global optimality of solutions obtained by local nonlinear programming (NLP) algorithms. Moreover, using global optimization algorithms is often not feasible because they typically require large computational efforts. This paper proposes the use of convex relaxations based on McCormick envelopes for the Refinery Operations Planning Problem (ROPP) that can be used to generate both initial solutions for the ROPP and to estimate optimality gaps for the solutions obtained. The results obtained using data from a real-world refinery suggest that the proposed approach can provide reasonably good solutions for the ROPP, even for cases in which there was no solution available using traditional local NLP algorithms. Furthermore, compared with a global optimization solver, the proposed heuristic is capable of obtaining better solutions in less computational time. [ABSTRACT FROM AUTHOR]

This paper proposes a gap-metric-basedmultiple model predictivecontrol (MMPC) method for nonlinear systems with a wide operatingrange. The gap metric theory, integrated into a neighborhood estimationalgorithm, is utilized to partition the whole operating range intosubregions corresponding to operating points. A local controller isthen designed in each subregion and is composed of a constant feedbackcontrol and a receding infinite-horizon control with an estimatedpolyhedral stable region. To guarantee the global stability of thewhole system, we design a novel switching rule activating betweenlocal controllers. The simulation study on a continuous stirred-tankreactor (CSTR) is presented to validate the proposed methods. [ABSTRACT FROM AUTHOR]

The ever-increasing impact of molecular quantum calculations over chemical sciences implies a strong and urgent need for the elaboration of proper teaching strategies in university curricula. In such perspective, this paper proposes an extensive project for a student-driven, cooperative, from-scratch implementation of a general Hartree-Fock self-consistent-field code. The project is primarily (but not exclusively) intended for a one-semester laboratory course in graduate physical-chemistry curricula: students are divided into working groups devoted to the implementation of specific subroutines, which are then gradually assembled in the final code. The resulting program is not limited to the overused, trivial case of two-electron diatomic systems, but can treat arbitrary closed-shell molecules with first- and second-row atoms at the STO-3G basis-set level and includes very useful features such as geometry optimization, population analysis, and calculation of dipole moments. The heart of the program is the by now historical algorithm devised by S. F. Boys for evaluating generic molecular integrals through repeated differentiation of fundamental integrals involving only s-type Gaussian orbitals. Though highly inefficient and completely outdated from a computational point of view, this method is reasonably fast for small- and even medium-size molecules (for instance, fixed-geometry calculations on methane and benzene take about 1 s and 6 min, respectively, on a modest home computer). More importantly, unlike newer advanced algorithms, it can be easily understood and implemented by students: thus, it becomes particularly appealing for a didactics based on reflexivity and active knowledge building, rather than on a comfortable but often premature and uncritical use of ready-made professional software packages. [ABSTRACT FROM AUTHOR]

In the last two decades, computational tools for mass spectrometry-based proteomics data analysis have evolved from a few stand-alone software solutions serving specific goals, such as the identification of amino acid sequences based on mass spectrometry spectra, to large-scale complex pipelines integrating multiple computer programs to solve a collection of problems. This software evolution has been mostly driven by the appearance of novel technologies that allowed the community to tackle complex biological problems, such as the identification of proteins that are differentially expressed in two samples under different conditions. The achievement of such objectives requires a large suite of programs to analyze the intricate mass spectrometry data. Our laboratory addresses complex proteomics questions by producing and using algorithms and software packages. Our current computational pipeline includes, among other things, tools for mass spectrometry raw data processing, peptide and protein identification and quantification, post-translational modification analysis, and protein functional enrichment analysis. In this paper, we describe a suite of software packages we have developed to process mass spectrometry-based proteomics data and we highlight some of the new features of previously published programs as well as tools currently under development. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Combiningmodel predictive control (MPC) with iterative learningcontrol (ILC) is a widely applied strategy in controlling repetitiveprocesses. Currently, there are two approaches for designing sucha controller, namely the separate design and the integrated design.The separate design has better performance on nonrepetitive disturbancerejection, while the integrated design has faster convergent rate.In this paper, by borrowing the idea of the two-stage optimization,a new design has been proposed for enhancing the integrated controlof combining MPC with ILC. In this way, good performance and fastconvergent rate can be obtained simultaneously. Simulations are givento illustrate the effectiveness of the proposed algorithm on disturbanceattenuation. [ABSTRACT FROM AUTHOR]

Hydrogenmanagement is important in refineries in achieving costcompetitiveness to produce clean and low-sulfur fuels. Other thanminimizing the fresh hydrogen requirement, it is extremely importantto reduce the compression work, required in any hydrogen allocationnetwork (HAN). In this paper, a rigorous targeting methodology tominimize compression work in a HAN is proposed. The proposed methodologyis algebraic and addresses flow, concentration, and pressure constraintsin HAN. On the basis of the rigorous mathematical arguments, it hasbeen proved that problem of minimizing compression work requirementin a HAN with multiple pressure levels can be broken into varioussubproblems having two pressure levels only and the compression workrequirement for two pressure levels can be minimized via minimizingcross-flows between these pressure levels. The applicability of theproposed algorithm is demonstrated through illustrative examples. [ABSTRACT FROM AUTHOR]

A variety of algorithms exist for optical single molecule tracking in two and three dimensions. One general class of algorithms employs cost-functionals to link the individual fluorescent spots, produced by a molecule in sequential video frames, into trajectories. This method has also been used to track one-dimensional (ID) molecular motions for relatively low diffusion rates (i.e., D < 1 μm2/s). At high diffusion rates, the cost-functional approach often fails to accurately reproduce ID trajectories, particularly when the molecules are closely spaced. In this paper, we present a new algorithm called trajectory-profile-guided (TPG) tracking that is designed specifically for ID trajectories. TPG tracking involves an initial search for one-dimensionally aligned fluorescent spots (i.e., candidate molecules). Qualifying candidates are subsequently identified and linked into trajectories based on several criteria. We test the TPG algorithm's accuracy and precision against cost-functional based tracking using both simulated and experimental video data. The results show that TPG tracking more accurately reproduces the actual ID trajectories, particularly at higher diffusion rates. TPG tracking is also shown to produce longer trajectories and more accurate estimates of trajectory aspect ratios (i.e., their dimensionality), molecular diffusion coefficients, and order parameters for aligned ID trajectories over a wide range of diffusion coefficients. [ABSTRACT FROM AUTHOR]

This paper is concerned with theidentification problems of thelinear parameter varying (LPV) system with missing output in the presenceof the time-delay. A multiple-model approach is adopted. Local modelsvarying from one operating point to another are first described byfinite impulse response (FIR) models. To handle missing output andtime-delay, the expectation-maximization (EM) algorithm is utilizedto estimate the unknown parameters and the time-delay simultaneously.Output Error (OE) models are widely used in controller design. Therefore,the auxiliary model principle is employed to recover the OE modelsbased on the initially identified FIR models. The EM algorithm isthen used again to refine the unknown parameters of the OE modelswith the complete data set to obtain the final global model. Simulationexamples are presented to demonstrate the performance of the proposedmethod. [ABSTRACT FROM AUTHOR]

Thispaper investigates the use of several nonlinear estimation algorithmssuch as extended Kalman filter (EKF), unscented Kalman filter (UKF),and cubature Kalman filter (CKF) in the problem of state estimationin chemical processes. Three simulation case studies are consideredto evaluate the performance of the proposed method. The second casestudy uses the experimental data to investigate the accuracy of theCKF against the UKF in practical applications. Simulation resultsconfirm the superiority of the CKF to the EKF and UKF. However, allof these approaches fail to handle the constraint issue in state estimationproblems. Subsequently, a modified CKF is introduced to overcome thelinear constraint in nonlinear estimation problems. The final partof the paper shows simulation results that confirm the effectivenessof the proposed constrained CKF (CCKF). Potential profits that canbe achieved while applying the proposed approach in constrained estimationproblems are shown compared to the conventional moving horizon estimation(MHE) algorithm. [ABSTRACT FROM AUTHOR]

Theintegrated cryogenic separation system (ICSS), which includesa chilling train and a demethanization section, is a crucial productionsystem in an ethylene plant. It accounts for up to 50% of the totalstart-up time and flare emissions of the whole ethylene plant. Traditionalstart-up scenarios are developed on experience and improved by trialand error, which is inefficient and dangerous. This paper employsrigorous dynamic simulations to examine the potential infeasibilitiesand operational risks of different start-up scenarios. The best start-upscenario with minimal start-up time and flare emissions is determined.To obtain the initial start-up state with ambient nitrogen fillingin the whole system, an initialization algorithm based on parametermodification for the dynamic ICSS model is presented. This novel initializationalgorithm greatly saves time and effort of the initialization of thestart-up model compared with previous studies. In addition, if time-relatedvariables are important for scenario study or temperature of the dynamicmodel changes greatly, heat capacity must be added into dynamic start-upmodel, which was not mentioned in published literatures. They arecritical to determine accurate start-up time, control the coolingrate, and verify the feasibility of scenarios during the dynamic start-upprocess under arbitrary temperature changes. The dynamic simulationprovides insight into process dynamic behavior, which is crucial forthe plant to evaluate and improve start-up scenarios. A real casestudy has demonstrated the efficacy of the dynamic simulation withheat capacity added. [ABSTRACT FROM AUTHOR]

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at .[Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

A commentary on a paper which proposed an algorithm to calculate the aggregated isotope distribution and center masses based on the molecular formula and elemental isotope distribution that could serve the ISOTOPICA software is presented. A list of the adjustments in Fourier transform approach that implements the center-mass calculation (FTMC) that influenced computational speed is given. The authors highlight the differences between the FTMC and the BRAIN algorithm.

A computational approach is essential whenever the complexity of the process under study is such that direct theoretical or experimental approaches are not viable. This is the case for protein folding, for which a significant amount of data are being collected. This paper reports on the essential role of in silico methods and the unprecedented interplay of computational and theoretical approaches, which is a defining point of the interdisciplinary investigations of the protein folding process. Besides giving an overview of the available computational methods and tools, we argue that computation plays not merely an ancillary role but has a more constructive function in that computational work may precede theory and experiments. More precisely, computation can provide the primary conceptual clues to inspire subsequent theoretical and experimental work even in a case where no preexisting evidence or theoretical frameworks are available. This is cogently manifested in the application of machine learning methods to come to grips with the folding dynamics. These close relationships suggested complementing the review of computational methods within the appropriate theoretical context to provide a self-contained outlook of the basic concepts that have converged into a unified description of folding and have grown in a synergic relationship with their computational counterpart. Finally, the advantages and limitations of current computational methodologies are discussed to show how the smart analysis of large amounts of data and the development of more effective algorithms can improve our understanding of protein folding. [ABSTRACT FROM AUTHOR]

In this paper, an optimal iterativelearning control (ILC) algorithmbased on a time-parametrized linear time-varying (LTV) model for batchprocesses is proposed. Utilizing the repetitive nature of batch processes,a time-parametrized LTV model is used to represent the nonlinear behavior,with its consistence and variance properties established. Furthermore,an optimal ILC algorithm based on the time-parametrized LTV modelis developed, and its convergence property is analyzed. Simulationshave demonstrated the effectiveness and excellent performance of theproposed method. [ABSTRACT FROM AUTHOR]

Generalized algorithmsare of great importance because they eliminatethe need to switch between methods and strategies for various cases;in fact, the same structure is useful after little modification. Inthis paper we introduce a scheme for modeling and controlling self-regulatingsystems. The purpose is to achieve a general but not optimal proportional-integral-derivative(PID) autotuner for first- and second-order transfer functions withor without time delays. A modeling algorithm based on a single runof the relay feedback test is proposed. The general procedure canbe applied to derive up to five unknown parameters for various cases.After the relay parameters are assigned, there is no need for a humanoperator; this feature as well as a short test time is the major advantageof the modeling phase of autotuning. A generalization of direct synthesisfor disturbance rejection is proposed for tuning a PID controller.The algorithm is modified to a 2 degrees of freedom structure so itcan provide a good set-point response and controller effort as well.The advantage of the structure as a general method is the need foronly one design parameter related to performance and robustness characteristics.Systematic ways to choose the design parameter will eliminate theneed for a human operator, which makes the scheme suitable for thetuning phase of autotuning. Simulation examples for the identificationphase show the accuracy of the identified models. Additionally, illustrativesimulations show the effectiveness of the generalized tuning methodfor low-order transfer functions. Also, examples clarify the priorityof the method to the well-known tuning method, internal model control,with the same degree of robustness. The autotuner is a general, unifiedstructure that unlike the previous methods does not alter modelingand tuning methods for various cases of low-order transfer functions. [ABSTRACT FROM AUTHOR]

In the pharmaceutical, fine chemical, polymer and foodindustry, where high value-added products are manufactured, insufficientcontrol might produce off-grade products. This can cause significantfinancial losses, or in the pharmaceutical industry, it can resultin an unusable batch. In these industries, batch reactors are commonlyused for the manufacturing of intermediate and final products. Thecontrol of these reactors is essentially a problem of temperaturecontrol. In the industry, an increasing number of heating–coolingsystems utilizing three different temperature levels can be found.Although they are advantageous from an economic point of view, itmakes the control more complicated. A pilot unit of such a systemcan be found in the authors’ laboratory, which contains a monofluidthermoblock with three different temperature levels. It is a usefulexperimental facility to test control algorithms for industrial use.This paper describes a split-range algorithm that can be used as partof the jacket temperature controller in the case of a thermoblockcontaining three different temperature levels. The main problem fromthe control point of view is that the sign of the gain of the controlledobject changes depending on the relation of the jacket and the actualfeed temperature of the jacket recirculation loop. The main purposeof the split-range algorithm is to maintain the sign of the gain ofthe controlled object unchanged, thus avoiding control instability.This solution ensures that the controller utilizes all three temperaturelevels, especially the medium level with lower energy consumption.The algorithm was tested with simulation, and for the jacket temperaturecontroller, a constrained PI controller was used. The parameters ofthe controller were identified by numerical optimization. The resultingjacket temperature controller extended with the split-range algorithmwas validated on the pilot system. The same set-point profile wasused for simulation and for the test measurement; this was designedto cover the entire temperature range of the system and to containdrastic changes, constant and ramped sections. Both in the case ofsimulation and the test measurement, good control performance wasachieved; the controlled variable followed the set-point with smallerror both in the case of ramped and constant set-points. [ABSTRACT FROM AUTHOR]

Water scarcity, rising energy costs, and stricter regulationsondisposal of industrial effluents have forced a focus on the waterusage in process plants. In this context, the development of methodologiesto analyze the possibility of water reuse in industrial processes,considering the presence of multiple contaminants, has became an importantissue for process engineers. There are a great variety of methodsto deal with this problem, and they can be divided in two groups:algorithmic methods and mathematical programming methods. The problemwith multiple contaminants becomes more complex, and the availablealgorithmic procedures are sometimes cumbersome; the methodologiesbased on mathematical programming present complex formulation andsolving algorithms. This paper presents an extension of the algorithmicprocedure water source diagram (WSD) to multiple contaminant processes.The proposed extended WSD procedure synthesizes water mass exchangenetworks for multiple contaminant processes, focusing on water reuse.It also targets the minimum water consumption and simultaneously makesthe synthesis of the corresponding water network. The proposed procedurewas applied in different examples used in the literature by othersauthors when discussing their proposals. Confrontation of resultsshows that the methodology here proposed has very good performance. [ABSTRACT FROM AUTHOR]

In this paper we present a new technique for measuringthe three-dimensional(3D) morphology of faceted crystals. This new technique is based onthe observation that a significant amount of regular reflection oflight happens from flat crystal faces. Innovative lighting and cameraarrangement is used to obtain the photograph of the crystal wherea face is highlighted by huge contrast from the background. Such photographsare used to identify the location of the face corners using an in-housemethod as well as with the well-known Harris corner detector. Thein-house method is found to be of comparable efficiency as the Harrisdetector. Using a simple algorithm, the 3D face coordinates are generatedfrom the two-dimensional face coordinates. Finally, the 3D coordinatesof the corners are used to reconstruct the crystal. A consistencycheck is employed during the reconstruction step which corrects forany missing corner or inconsistent data. [ABSTRACT FROM AUTHOR]

It has been over 60 years since Castaing (Castaing, R. Application of Electron Probes to Local Chemical and Crystallographic Analysis. Ph.D. Thesis, University of Paris, Paris, France, 1951; translated by P. Duwez and D. Wittry, California Institute of Technology, 1955) introduced the technique of electron probe X-ray microanalysis (EPMA), yet the community remains unable to quantify some of the largest terms in the technique's uncertainty budget. Historically, the EPMA community has assigned uncertainties to its measurements which reflect the measurement precision portion of the uncertainty budget and omitted terms related to the measurement accuracy. Yet, in many cases, the precision represents only a small fraction of the total budget. This paper addresses this shortcoming by considering two significant sources of uncertainty in the quantitative matrix correction models-the mass absorption coefficient, [μ/ρ], and the backscatter coefficient, η. Understanding the influence of these sources provides insight into the utility of EPMA measurements, and equally important, it allows practitioners to develop strategies to optimize measurement accuracy by minimizing the influence of poorly known model parameters. [ABSTRACT FROM AUTHOR]

This paper applies self-consistent field theory (SCFT)to discretepolymer chains consisting of a finite number of beads, N, joined together by freely jointed bonds of arbitrary potential, b(R). The numerics of this SCFT can beperformed efficiently using spectral or pseudospectral algorithms,permitting its application to complex morphologies. To demonstrateits effectiveness, we examine diblock copolymer melts where the polymerbonds have a fixed length, a, and the nonbonded interactionshave a finite range, σ, with a strength controlled by the standardFlory–Huggins χ parameter. Although the results reduceto those of the usual SCFT for Gaussian chains in the limit of large Nand small χ, there are some notable differencesfor short chains with strong interactions. The most significant involvesthe internal interfaces, which in turn affects the size of the domains.Furthermore, the finite range of the nonbonded interactions, necessaryto properly treat the internal interfaces, causes a noticeable shiftof the ODT toward larger χN. As χ becomesvery large, particularly at small N, the finite extensibilityof the freely jointed chains restricts the size of the domains, whichleads to a preference for the lamellar phase. [ABSTRACT FROM AUTHOR]

In this paper, a full scale cold model of 500 t/d entrained-flowgasifier by two-stage oxygen was used to analyze the distributioncharacteristics of the aerodynamic field in the gasifier. The realizable k–ε turbulent model was adopted to simulatethe three-dimensional turbulent flow field with the SIMPLE (semi-implicitmethod for pressure-linked equations) algorithm for the gasifier.The effects of side injecting velocity, position, number of nozzles,and group mode on aerodynamic field distributions were analyzed. Theresults show that the numerical simulation data are well-matched withthe experiment data. The simulated results are acceptable and canpresent a foundation for the future design, assessment, and furtheroptimization of the entrained-flow gasifier by two-stage oxygen. [ABSTRACT FROM AUTHOR]

Inspired by the biologic nature of flying geese, processgoosequeue (PGQ) approaches toward plantwide process optimization are explicitlyintroduced in this paper along with applications in real-time optimization(RTO). Taking advantage of ad-hoc PGQ metrics, process variables associatedwith a process unit could be accordingly identical with geese positionsof a PGQ. Motivated by the self-organization in flight formation ofgeese, a process unit can achieve such an optimum formation that everygoose in the PGQ benefits from the maximum upwash. In this sense,adjustment rules invoked to track the ideal PGQ formulation are accommodated.Followed by this idea, a plantwide process is first decomposed intoseveral hierarchically connected multilayer PGQs. Subsequently, aplantwide PGQ which includes a PGQ-objective and several multilayerPGQs is constructed, which contributes to solving complex plantwideprocess optimization problems in a novel way. As applications of PGQapproaches, we initially address a supervision-driven RTO issue concerningeconomic performance deterioration caused by process supervision.A process unit whose variables are shifted by human operators canbe regarded as an ill-PGQ which would trigger the autonomous adjustmentsof the plantwide PGQ. Enabling algorithms concerning adjustment sequenceof the plantwide PGQ with an ill-PGQ are constructed, which are generallycharacterized by ill-PGQ detection, PGQ follow-up, and PGQ-objectiveachievement. To demonstrate the feasibility and validity of this contribution,the Tennessee Eastman (TE) benchmark process is employed as an extensivecase study, showing that the proposed approaches particularly enjoyconsiderable computational simplicity in contrast with traditionalglobal optimization strategies. [ABSTRACT FROM AUTHOR]

This paper introduces a practical scheduling of multipipelinecrudeoil blending (SMCOB) problem. It is formulated as a complex mixed integer nonlinear programming (MINLP) model, taking the charging sequenceand flow rates of oil tanks as decision variables, which cannot beefficiently solved by traditional deterministic methods and solvers.Then, a novel two-level optimization framework based on constrainedordinal optimization (COO) and evolutionary algorithms (EA) is proposed.The solution methodology has two stages based on the main proceduresof COO. At the crude evaluation stage, discrete EA are used to searchfor sequence solutions in the outer level. It evolves the sequencesolutions on the basis of their rough evaluation of the feasibilityand objective value obtained from the inner level and keeps certain numberof probably best sequence solutions. At the accurate evaluation stage,the probably best sequence solutions kept by the crude evaluationstage are accurately evaluated by inner-level continuous EA. The COOapproach ensures that some true good enough sequence and flow ratesolutions can be obtained from the accurate evaluation stage withhigh probability. COO-based EA are compared with mixed-coding EA toverify the framework’s efficiency and robustness. [ABSTRACT FROM AUTHOR]