Optimization strategy of the multihole products weld lines based on the multi‐objective evaluation system and kriging surrogate model

The plastic multihole products are widely used in Household Appliance and Automobile industry. The densely distributed weld lines resulting from the multiaperture structure seriously damage the surface appearance and mechanical properties of the products. However, there are relatively few studies on the optimization of weld lines of the plastic multihole products. Therefore, a multihole plate was taken as an example to exploit an optimization strategy for this kind of defects. The multi‐objective evaluation system was established to assess bonding quality of weld lines based on Numerical Simulation. And a multi‐objective optimization methodology was presented to efficiently optimize the process parameters. First, back propagation neural networks and Kriging method were combined to build the surrogate models based on the experiments arranged by Latin hypercube sampling. Second, a systematic test strategy was proposed to ensure high accuracy of the surrogate model. Then, the pareto optimal solution was obtained by integrating the surrogate model and Non‐dominated Sorting Genetic Algorithm II. Finally, the simulated and practical confirmation experiments indicate that bonding quality of weld lines can be effectively characterized and significantly improved by the multi‐objective evaluation system and the multi‐objective optimization methodology, respectively. POLYM. ENG. SCI., 59:781–790, 2019. © 2018 Society of Plastics Engineers     

Inferential Control of Reactive Destillation Columns – An Algorithmic Approach

Decentralized control system design comprises the selection of a suitable control structure and controller parameters. Here, mixed integer optimization is used to determine the optimal control structure and the optimal controller parameters simultaneously. The process dynamics is included explicitly into the constraints using a rigorous nonlinear dynamic process model. Depending on the objective function, which is used for the evaluation of competing control systems, two different formulations are proposed which lead to mixed‐integer dynamic optimization (MIDO) problems. A MIDO solution strategy based on the sequential approach is adopted in the present paper. Here, the MIDO problem is decomposed into a series of nonlinear programming (NLP) subproblems (dynamic optimization) where the binary variables are fixed, and mixed‐integer linear programming (MILP) master problems which determine a new binary configuration for the next NLP subproblem. The proposed methodology is applied to inferential control of reactive distillation columns as a challenging benchmark problem for chemical process control.     

On‐line monitoring of chord distributions in liquid–liquid dispersions and suspension polymerizations by using the focused beam reflectance measurement technique

The on‐line monitoring of the droplet/particle size distributions is very important to ensure the quality and applicability of various products in heterogeneous systems. For this reason, the main objective of the present work was to study the usage of the focused beam reflectance measurement (FBRM) technique for monitoring of liquid–liquid dispersions (styrene dispersion in aqueous solutions) and suspension polymerization of styrene. To do better understand the FBRM technique in these systems, the effects of surfactant concentrations, agitation speed and ambient light were evaluated during the in‐line monitoring of average chord lengths and chord‐length distributions (CLD) at different operation conditions in batch experiments. In addition, a preliminary investigation of the optimal probe position was conducted in the polymerization experiments. It is shown that the FBRM technique is sensitive to variations of particle sizes in the characteristic ranges of particle diameters of typical styrene suspension polymerizations, being useful for monitoring and also control applications that require the on‐line characterization of CLD in real time in liquid–liquid dispersions and polymerization systems. POLYM. ENG. SCI., 56:309–318, 2016. © 2015 Society of Plastics Engineers     

Optimal cure steps for product quality in a tire curing process

Numerical algorithms and computer programs have been developed to determine optimal cure steps in a tire curing process. A dynamic constrained optimization problem was formulated with the following ingredients: (1) an objective function that measures product quality in terms of final state of cure and temperature history at selected points in a tire; (2) constraints that consist of a process model and temperature limits imposed on cure media; (3) B‐splines representation of a time‐varying profile of cure media temperature. The optimization problem was solved using the complex algorithm along with a finite element model solver. Numerical simulations were carried out to demonstrate the procedure of determining optimal cure steps for a truck/bus radial tire. © 1999 John Wiley & Sons, Inc. J Appl Polym Sci 74: 2063–2071, 1999     

Optimization‐Based Nonlinear Centralized Controller Tuning of Liquid‐Liquid Extraction Processes

Abstract

The control problem of an agitated contactor is considered in this work. A Scheibel extraction column is modeled using the non‐equilibrium backflow mixing cell model. Model dynamic analysis shows that this process is highly nonlinear, thus the control problem solution of such a system needs to tackle the process nonlinearity efficiently. The control problem of this process is solved by developing a multivariable nonlinear control system implemented in MATLAB?. In this control methodology, a new controller tuning method is adopted, in which the time‐domain control parameter‐tuning problem is solved as a constrained optimization problem. A MIMO (multi‐input multi‐output) PI controller structure is used in this strategy. The centralized controller uses a 2×2 transfer function and accounts for loops interaction. The controller parameters are tuned using an optimization‐based algorithm with constraints imposed on the process variables reference trajectories. Incremental tuning procedure is performed until the extractor output variables transient response satisfies a preset uncertainty which bounds around the reference trajectory. A decentralized model‐based IMC (internal model control) control strategy is compared with the newly developed centralized MIMO PI control one. Stability and robustness tests are applied to the two algorithms. The performance of the MIMO PI controller is found to be superior to that of the conventional IMC controller in terms of stability, robustness, loops interaction handling, and step‐change tracking characteristics.     

A Simplified Numerical Approach to Feedback Controller Design

A generalized parameter optimization method for computing feedback controller parameters is proposed. The method utilizes the downhill simplex method (DSM), a pattern search algorithm, to determine the optimal parameters that minimize an objective function or performance index. The system model, expressed in terms of state‐space equations is integrated with respect to time at each DSM iteration in order to determine the states. A fourth‐order Runge‐Kutta scheme is used for integrating the state equations. A penalty function approach is used for problems with inequality constraints on the state variables or controls. Though relatively inefficient in terms of the number of function evaluations, DSM requires only that the user provide the model equations, and not their derivatives. Additionally, the DSM code is very compact. Thus, a small and straightforward program allows for controller parameter determination for a variety of state‐space and classical PID feedback control design problems.     

Development of New Cordierite‐Based Refractory Coatings for Casting Applications

In this study, new refractory coatings based on synthesized cordierite for the casting applications were developed. The investigation included starting raw materials characterization, synthesis of the cordierite, design of the refractory coating as final product, and its application testing. The obtained results pointed out that coating suspension sediment stability was crucial quality parameter. Design and optimization of the coatings composition, with controlled rheological properties included, were achieved by application of different coating components, namely different suspension agents and by alteration of the coating production procedure. Cordierite, used as filler, was obtained by means of synthesis in the solid‐state reaction on the basis of talc, kaolin, and alumina. The investigation showed that the application of these particular types of water/alcohol‐based coatings has positive influence on surface quality and structural and mechanical properties of the castings of aluminum alloys obtained by casting into sand molds by means of evaporable models method, that is, evaporate pattern casting process.     

Optimal quality control of baker's yeast drying in large scale batch fluidized bed

Optimal quality control of drying process of baker's yeast in large scale batch fluidized bed dryer is presented using neural network based models and modified genetic algorithm (GA). The objective of this study is to determine optimal conditions to maximize product quality while minimizing energy consumption. For this purpose, the drying process and quality models based on neural network with delay units are combined for predicting the dry matter, product temperature, change in dry matter and the quality loss while minimizing energy consumption and this model is then used for optimal quality control. A stochastic method based optimization structure is designed in order to solve the optimization problem whose the objective function is discontinuous, non-differentiable, complex and highly non-linear. The results obtained by optimal quality control based on modified GA showed that the performance of the existing industrial scale drying process was improved. The constructed optimal quality control structure is very convenient for the production process applications and may be applied without too much modification.     

Practical improvements to autocovariance least‐squares

Identifying disturbance covariances from data is a critical step in estimator design and controller performance monitoring. Here, the autocovariance least‐squares (ALS) method for this identification is examined. For large industrial models with poorly observable states, the process noise covariance is high dimensional and the optimization problem is poorly conditioned. Also, weighting the least‐squares problem with the identity matrix does not provide minimum variance estimates. Here, ALS method to resolve these two challenges is modified. Poorly observable states using the singular value decomposition (SVD) of the observability matrix is identified and removed, thus decreasing the computational time. Using a new feasible‐generalized least‐squares estimator that approximates the optimal weighting from data, the variance of the estimates is significantly reduced. The new approach on industrial data sets provided by Praxair is successfully demonstrated. The disturbance model identified by the ALS method produces an estimator that performs optimally over a year‐long period. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1840–1855, 2015     

Colorimetric characterization of a computer‐controlled liquid crystal display

A new method was used to characterize computer‐controlled liquid crystal displays (LCDs). The characterization, which was performed to enable colorimetric image display, included channel independence, spatial independence, screen uniformity, and colorimetry. The colorimetric model consisted of three one‐dimensional look‐up tables (LUTs) describing each channel's optoelectronic transfer function and a 3 × 4 matrix transformation that included black‐level flare. The matrix coefficients were estimated statistically by minimizing the average CIEDE2000 color difference for a data set sampling the display's colorimetric gamut. The LUTs were recreated dynamically throughout the optimization of the matrix coefficients. The characterization was implemented with three different instruments to evaluate the robustness of the method with respect to measurement uncertainty. The average performance ranged between 0.1 and 0.4 ΔE₀₀ and was well correlated with instrument precision. The optimization approach improved performance by a factor of two compared with direct measurements. Despite differences in instrument design, the chromaticities of each primary following optimization and black‐level flare compensation were very similar. This excellent performance was a result of the display's optoelectronic properties well matching the model assumptions. The technique was also used to characterize three additional LCD displays ranging in their matching of the model assumptions. In this case, performance worsened. For one display, more complex models would be required for colorimetric characterization. Finally, a colorimetric characterization based on measurements at the center of the display and perpendicular to the face was used to predict measurements at the edges and at different angles. The results indicated that characterizations would be required at multiple positions and angles in order to achieve sufficient accuracy. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 365–373, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20046     

A delay‐dependent robust fuzzy MPC approach for nonlinear CSTR

Based on Takagi–Sugeno (T–S) fuzzy models, a robust fuzzy model predictive control (MPC) algorithm is presented for a class of nonlinear time‐delay systems with input constraints. Delay‐dependent sufficient conditions for the robust stability of the closed‐loop system are derived, and the condition for the existence of the fuzzy model predictive controller is formulated in terms of nonlinear matrix inequality via the parallel distributed compensation (PDC) approach. By using a novel matrix transform technique, a receding optimization problem with linear matrix inequality (LMIs) constraints is constructed to design the desired controllers with an on‐line optimal receding horizon guaranteed cost. Finally, an example of continuous stirred tank reactors (CSTR) is given to demonstrate the effectiveness of the proposed results.     

Coloration properties and chemo‐rheological characterization of a dioxazine pigment‐based monodispersed masterbatch

A dioxazine‐based color pigment was added to a commercial polyamide 6 (PA6) through an extrusion process, in order to prepare monoconcentrated violet masterbatches through different production set‐up. A detailed characterization of the resulting materials was carried out in order to find the best processing parameters combination to optimize pigment dispersion and to reduce the clogging power. The preparation of masterbatches with repeated extrusions markedly reduced the filter pressure value and increased the Relative Color Strength, while filtration did not significantly influence pigment dispersion. Rheological measurements and end‐groups analysis were conducted on the same materials with the aim to evaluate their thermal degradation resistance, and the thermal stability of the compounds was retained even upon three extrusions. Therefore, it can be concluded that a proper optimization of the process parameters could lead to an important reduction of the production waste, increasing the quality of the final product. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41452.     

Transition from Batch to Continuous Operation in Bio‐Reactors: A Model Predictive Control Approach and Application

This work considers the problem of determining the transition of ethanol‐producing bio‐reactors from batch to continuous operation and subsequent control subject to constraints and performance considerations. To this end, a Lyapunov‐based non‐linear model predictive controller is utilized that stabilizes the bio‐reactor under continuous mode of operation. The key idea in the predictive controller is the formulation of appropriate stability constraints that allow an explicit characterization of the set of initial conditions from where feasibility of the optimization problem and hence closed‐loop stability is guaranteed. Additional constraints are incorporated in the predictive control design to expand on the set of initial conditions that can be stabilized by control designs that only require the value of the Lyapunov function to decay. Then, the explicit characterization of the set of stabilizable initial conditions is used in determining the appropriate time for which the reactor must be run in batch mode. Specifically, the predictive control approach is utilized in determining the appropriate batch length that achieves stabilizable values of the state variables at the end of the batch. Application of the proposed method to the ethanol production process using Zymomonas mobilis as the ethanol producing micro‐organism demonstrates the effectiveness of the proposed model predictive control strategy in stabilizing the bio‐reactor.     

A process performance index and its application to optimization of the rtm process

Resin transfer molding (RTM) is a promising manufacturing process for hig formance composite materials. However, the fact that RTM process design has traditionally been an expensive, time‐consuming trial‐and‐error procedure has p ited its wide application base. This paper proposes a solution to that problem—a simulation‐based optimum process design scheme for RTM. This scheme ei engineers to determine the optimum locations of injection gates and vents so both process efficiency and high part quality can be ensured. Essential to this mum process design scheme is a process performance index, which is defined respect to the major factors influencing RTM process efficiency and part quality This index is then used as the objective function for the RTM process design optimization model. Gate and vent locations are the process design parameters optimized. All data is obtained by running an RTM simulation program, and th netic algorithm (GA) is employed to carry out the optimization procedure for design parameters. It is found that constant pressure optimization will yi process with a short flow path, whereas constant flow optimization will yield process with smooth and vent‐oriented flow pattern. Although there is no dry factor in the objective function, it is interesting to note that both constant pres and constant flow optimization procedures result in process designs with a mil mum probability of dry spot formation. This study finds that, in general, cons flow optimization should be employed if injection pressure is not a major cone otherwise, constant pressure optimization should be used. Two case studies presented to illustrate the efficacy of this approach.     

An optimization framework to combine operable space maximization with design of experiments

The introduction of Quality by Design in the pharmaceutical industry stimulates practitioners to better understand the relationship of materials, processes and products. One way to achieve this is through the use of targeted experimentation. In this study, an optimization framework to design experiments that effectively leverage parameterized process models is presented to maximize the space covered in the output variables while also obtaining an orthogonal bracketing study in the process input factors. The framework considers both multi‐objective and bilevel optimization methods for relating the two maximization objectives. Results are presented for two case studies—a spray coating process and a continuously stirred reactor cascade—demonstrating the ability to generate and identify efficient designs with fit‐for‐purpose trade‐offs between bracketed orthogonality in the input factors and volume explored in the process output space. The proposed approach allows a more complete understanding of the process to emerge from a small set of experiments. © 2018 The Authors. AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers. AIChE J, 64: 3944–3957, 2018     

Integration of Design and Control: A Robust Control Approach Using MPC

This paper presents a new method to integrate process control with process design. The process design is based on steady‐state costs, .i.e., capital and operating costs. Control is incorporated into the design in terms of a variability cost. This term is calculated based on the non‐linear process model, represented here as a nominal linear model supplemented with model parameter uncertainty. Robust control tools are then used within the approach to assess closed‐loop robust stability and to calculate closed‐loop variability. The integrated method results in a non‐linear constrained optimization problem with an objective function that consists of the sum of the steady costs and the variability cost. Optimization using the traditional sequential approach and the new integrated method was applied to design a multi‐component distillation column using a Model Predictive Control (MPC) algorithm. The optimization results show that the integrated method can lead to significant cost savings when compared to the traditional sequential approach. In addition, an RGA analysis was performed to study the effects of process interactions on the optimization results.     

Real‐time Dynamic Optimization of Non‐linear Batch Systems

In this paper, a methodology for designing and implementing a real time optimizing controller for non‐linear batch processes is discussed. The controller is used to optimize the system input and state trajectories according to a cost function. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov‐based extremum seeking approach is used to compute the trajectory parameters. Smooth trajectories were generated with reduced computing time compared to many optimizations in literature. In this paper, the theory is applied to general non‐flat non‐linear systems in a true on‐line optimization.     

A property‐based approach to the synthesis of material conservation networks with economic and environmental objectives

This article presents a multiobjective optimization model for the recycle and reuse networks based on properties while accounting for the environmental implications of the discharged wastes using life‐cycle assessment. The economic objective function considers fresh sources and treatment costs, whereas the environmental objective function is measured through the eco‐indicator 99. The model considers constraints in the process sinks as well as in the environment based on stream properties such as pH, chemical oxygen demand, toxicity, density, and color, in addition to the composition of the waste streams. A global optimization procedure is developed by indirectly tackling properties through property operators and by segregating the process streams before treatment. Three examples are included, and the results show that it is possible to consider simultaneously the trade‐offs between the total annual costs and the overall environmental impact using the proposed methodology. © 2010 American Institute of Chemical Engineers AIChE J, 2011