Optimization Of Complex Processes Research Articles

Simultaneous topology design and optimization of PDE constrained processes based on mixed integer formulations

Simultaneous topological design and optimization of complex processes that are described by partial differential equations is a challenging but promising research area. Widely adopted nested and sequential approaches are mostly applicable based on heuristic solutions, hindering the theoretical improvement potential due to decentralized decision-making in subsequent stages with a significant number of trial-and-error procedures. This study introduces a mixed integer formulation addressing the governing equations and case-dependent topological constraints at each discretization point, enabling solutions through rigorous solvers under process-related constraints and objectives. Nonlinear expressions in the formulations are further tailored using piecewise linear approximations, still representing the major nonlinear trends through a mixed-integer linear nature to favor global optimality and benefit from computational advancements, when needed. Heat and Stokes flow problems are used as case studies to demonstrate the applicability of the methodology.

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. The quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

Acquisition Function Choice in Bayesian Optimization via Partially Observable Markov Decision Process

Bayesian optimization is a popular technique in optimization of functions which are expensive to evaluate (e.g. in experimental optimization of complex and slow processes). Bayesian optimizers select the points in which the cost function is evaluated via optimizing a surrogate acquisition function. Such acquisition function is usually fixed, based on intuition, from several choices available in the literature. However, for a given problem, it is sometimes difficult to know which is the right one and, perhaps, a strategy combining them along the experiments sequence might provides better results. This paper explores a scenario-based optimization to estimate the best sequence of acquisition function choices in finite-horizon optimization problems under uncertainty, cast as a Partially Observable Markov Decision Process. Improvements over standard Bayesian optimization and a recent rollout strategy are illustrated in simulation.

SVM MODEL FOR PREDICTION OF ELECTROCHEMICAL DIAMOND DRILLING RESPONSE PARAMETERS

Machining of advanced materials is difficult with traditional machining methods so the development of new and hybrid nontraditional machining processes is being done. These hybrid processes have complex machining process parameters and optimization of these is very necessary for better machining characteristics. Here a Support Vector Machine model is utilized to develop a prediction model for electrochemical diamond drilling process. The prediction accuracy achieved with SVM model was better than the developed quadratic regression model.

Integrated Process of Ammonium Ion Adsorption by Natural Dispersed Sorbents

The process of adsorption treatment of effluents from ammonium ions is considered as an integrated two-stage process consisting of the stage of adsorption of contaminants by natural sorbents in the apparatus with a stirrer and the liquid separation stage and solid phases. Mathematical models of ammonium ions adsorption from effluents by natural dispersed sorbents are proposed, based on the assumption that the process is described by Langmuir and Friendlich isotherms. The values of ion exchange equilibrium constants for different types of natural sorbents have been established by identifying experimental data for theoretical dependences. The obtained constants can be used to calculate the average concentration of ammonium ions in the solution and in the grain of the sorbent in the process of integrated adsorption process. Based on the analysis of the research results, the optimum method of the spent sorbent separation was selected - separation of the suspension of purified ammonium-containing effluents - spent sorbent under the action of gravity. Indicators of optimization of complex process of sewage treatment from ammonium ions are offered.

Using Proactive Maintenance Strategy for Sustainable Electric Power Production in Nigeria

An unpleasant turn of events would compel and prevail upon power plants owners and operators looking for alternative for keeping maintenance activities up and awake. Thus, causing a break away from outdated traditional system originally practiced in maintenance organizations, which affects operations in terms of costs and energy required for sustainable activity in the power production industry; via Generation, Transmission and Distribution. However, the only inevitably obtainable option as per organizational success in the power industry is through a substituent for a failure based system with success based strategy that is concerned with optimization of complex processes, systems or organization by developing, improving and implementing integrated systems of people, money, knowledge, information and equipment, central to manufacturing and production operations. A platform intended to deliver on plant (a) availability (b) reliability and (c) sustainability, which lead to reduced maintenance costs and increased profitability. Keywords: maintenance management; strategy implementation, power sector, performance optimization DOI: 10.7176/IEL/11-3-03 Publication date: October 31 st 2021

Effect of air velocity, temperature, and relative humidity on drying kinetics of rubberwood

Kiln drying of rubberwood lumbers is a complex transport phenomenon for realistic modeling and simulation. To decouple this complexity, researchers usually divide their research into two parts. The first one is single-lumber drying kinetics to describe how wood lumber responds to its surface conditions. Then they combine this drying kinetics with a lumped transport model or dispersion model or computational fluid dynamics. The mathematical models are then solved numerically to predict the industrial kiln drying behaviors. This work focuses on the drying kinetics of stacked rubberwood lumbers using hot air at different air velocity (0.5, 1.5, 2.5, 3.5, 4.0 m/s), relative humidity (6–67% relative humidity (RH)) and temperature (60–100 °C). The drying kinetics followed the conventional drying theory. However, the two drying periods, namely constant and falling rate (CRP and FRP), were not distinct. As the air velocity increased, the transition from CRP to FRP is faster. The middle of the transition period (at critical moisture content, CMC) moves closer to the fiber saturation point (FSP). The overall mass transfer coefficients in the falling rate period for stacked rubberwood drying were lower than those predicted by the Ananias correlation. Hence, a modified formula was proposed, representing the overall moisture transfer coefficients as a function of air velocity, temperature, relative humidity, and lumbers thickness for the range of variables under investigation satisfactorily. In general, the drying rate and the overall moisture transfer coefficient increased with increasing air velocity, drying temperature, and decreasing RH. Relative humidity directly affects the driving force of moisture transfer rate because higher RH is associated with higher equilibrium moisture content. A lumped parameter model for kiln drying was also developed. After being integrated with the estimated mass transfer coefficient, the model can predict the moisture profiles in lab-scale kiln drying satisfactory, although the model needs more validation data. These kinetic parameters and correlation for stacked rubberwood drying can be used in more complex models and process optimization in future research.

Industrial Engineering for Healthcare Management – Example Lean Management and ICT Tools

Abstract Industrial engineering is a field dealing with optimization of complex processes, systems, or organizations by developing, improving and implementing integrated systems of people, money, knowledge, information, equipment, energy, and materials. Hence, the scope of industrial engineering is wide and includes various fields, from manufacturing, through banking, different types of services, to administration and healthcare. Various industrial engineering tools could be implemented in healthcare settings. The use of such tools is popular in western economies. For example, simulation modelling of services is popular in the US. However, there is still a very limited number of case studies on the application of such tools in healthcare that would consider the Polish economy. The aim of this paper is to present selected successful applications of lean management tools in the Polish healthcare. This may serve as an inspiration for healthcare organizations to search for and implement methodological approaches to improve their services.

Optimization of Induction Quenching Processes for HSS Roll Based on MMPT Model

To improve the comprehensive performance of high speed steel (HSS) cold rolls, the induction hardening processes were analyzed by numerical simulation and experimental research. Firstly, a modified martensitic phase transformation (MMPT) model of the tested steel under stress constraints was established. Then, the MMPT model was fed into DEFORM to simulate the induction quenching processes of working rolls based on an orthogonal test design and the optimal dual frequency of the induction quenching process was obtained. The results indicate that the depth of the roll’s hardened layer increases by 32.5% and the axial residual tensile stress also becomes acceptable under the optimized process. This study provides guidance for studying phase transformation laws under stress constraints and the optimization of complex processes in an efficient manner.

Individual cost accounting in the management of medical organizations

In the modern economic conditions, the rational planning of costs and the complex process optimization are essential requirements to all organizations. Knowledge of costs is needed to correctly assess the economic performance of an organization. Competent and timely correction of tariffs for the obligatory medical insurance and rationalization of the requested financing of the medical organization depends on this assessment. In the present study, we analyze various methods of personalized cost accounting: the ratio of costs to charges (RCC); relative value unit (RVU); time-driven activity-based costing (TDABC), and the possibility of their adaptation to the specific needs of medical organizations. The personalized cost accounting incorporated into a medical information system allows for controlling, planning and carrying out a close internal management of financial activity. This function helps decision-makers: control the use of funds for medical care provision; increase the efficiency of management decisions; justify the prices of paid medical services; define the deficit and surplus work units; analyze the treatment cost for each patient, considering the diagnosis, method of treatment, age and other classification signs, including the reference to specialized departments; reduce the unnecessary “paper” work load on the medical personnel; model the future needs of the organization in accordance with the planned changes in the hospitalization policy; optimize, control and plan the budget with regard to the established standards of financial expenses. Implementation of this approach is expected to increase the work efficiency in most medical organizations and the entire healthcare system.

Research on PTC Cable Materials Based on Principal Component Analysis and Quantitative Correspondence Factor Analysis Method in Big Data Technology

To analyse the huge production data scientifically, the records of enterprises were used. Principal component analysis techniques and quantitative corresponding factor analysis techniques in big data technology were applied. The primary and secondary factors affecting the design performance of PTC cable materials and the influence laws were found. Through analysis, the best process recipe conditions in the existing data were obtained. The results showed that the optimization of the PTC cable material formulation process effectively guided industrial production and met different practical needs. In summary, multi-factor and multi-level visual design and analysis methods, artificial neural network models and big data technology have good qualitative and quantitative analysis functions. A complex process optimization problem with four influencing factors and three indicators is solved.

In-situ generation of effective coagulant to treat textile bio-refractory wastewater: Optimization through response surface methodology

The discharge of textile industries wastewaters into waterbodies remains a constant threat for environment. Through this study, we aim to optimize Textile Bio-refractory Wastewater (TBW) treatment by electrocoagulation (EC) process. EC runs were carried out in batch mode using an electrolytic cell equipped with iron electrodes, in parallel monopolar (MPP) configuration. TBW’s characterization has revealed a low biodegradability index value (BOD5/COD = 0.02). Most of optimizations studies have focused on traditional “one-factor-at-a time” methodology. However, this approach is visibly time consuming, does not take into account interactions effects and could give unreliable results. Therefore, we opted for Response surface methodology (RSM) which is a powerful statistical-based technique for modelling and optimization of complex processes. In this perspective, factorial experimental design (FD: 2k=3) was used to determine the influent parameters on textile pollutants removal. Electrolysis time (X1) and Current intensity (X2) were the main influential parameters with a cumulative contribution superior to 94 % for each of the three responses studied (turbidity, COD, Absorbance) whereas interactions and pH contributions are almost negligible (< 4% for pH). Afterwards, a central composite design (CCD) has been used to determine optimal conditions that maximize simultaneously all responses. Experimental results predicted by RSM model (99.95 % of Turbidity; 78.87 % of COD; 84.91 % Absorbance) under optimal conditions (75 min; 2.61 A and pH = 6.83) were close to the theoretical ones. EC process operated under optimal conditions involves a total cost of 4.95 US$/ m3 of treated TBW including chemicals, energy and electrode consumption.

To the special Issue on “Metaheuristics for optimization of complex process engineering”

To the special Issue on “Metaheuristics for optimization of complex process engineering”

Three-dimensional analysis of medium-frequency induction heating of steel pipes subject to motion factor

As hydrocarbon resource exploration will extend towards deep sea and alpine cold areas in future, the corresponding welded pipes for oilfield use will face increasingly adverse operating environment. Acquisition of more precise heat source for medium-frequency heat treatment of welded pipes is fundamental to postweld residual stress analysis, weld microstructure improvement and performance research. However, as a pipe billet is subjected to movement factor, it is very tough to solve for a three-dimensional heat source closer to real pattern with complex process parameters. In this paper, the authors utilized a computing method of sequential coupling combining electromagnetic-thermal coupling and relative motion between steel pipe and coils, and designed the corresponding computation flowchart, and by computing pipe billet heat treatment process, they found that the steady-state temperature field formed around a weld appears double ellipsoidal. Further analysis shows that the root cause of the double ellipsoidal temperature field is asymmetric distribution of magnetic flux density and induction current, generated in the weld zone due to electromagnetic effect, within the steel pipe in axial direction. Moreover, stability and hysteresis of pipe billet temperature field were studied. It’s found that there is a certain hysteresis between the maximum temperature of inner surface and that of outer surface, because of two different heat sources undergone by the inner and outer surfaces of the weld, as well as relative motion between induction coil and welded pipe. The unique heat transfer process and temperature distribution pattern in wall-thickness direction is the immediate cause of the double ellipsoidal three-dimensional temperature field. This study offers a referenceable analysis method for further exploring steel pipe induction heating process under complex process parameters and optimization of the process parameters.

A Multi-agent Approach to Computational Optimization of Metal Forming Processes

In the paper we present a new original approach to a complex multi-phase process optimization problem that relies on dividing the optimization task into partial tasks related to the implementation of individual phases. To implement this concept, we propose a multi-agent approach. Its practical realization is shown on the example of manufacturing process for auto body parts made of the Advanced High Strength Steels (AHSS). Although it is a rather simple process consisting of only two phases, we assumed, however, that the results obtained will allow to extend further research to more complex problems. We present the operating principles of a multi-agent model, the flow of the messages between agents, and the architecture of the system. To ensure the proper speed of the whole system a simple and flexible multi-agent framework called Eve was used to develop a prototype of the system. Research performed, as well as preliminary tests have shown that a multi-agent approach can be successfully applied to reduce complexity of the whole optimization processes. Due to splitting a single, complex optimization process into several, partially independent optimization processes, delegating them to autonomous agents, and application of a knowledge-based reasoning system, significant advantage could be observed over to the solutions described so far in the literature.

An efficient latent variable optimization approach with stochastic constraints for complex industrial process

For complex chemical processes, process optimization is usually performed on causal models from first principle models. When the mechanism models cannot be obtained easily, restricted model built by process data is used for dynamic process optimization. A new strategy is proposed for complex process optimization, in which latent variables are used as decision variables and statistics is used to describe constraints. As the constraint condition will be more complex by projecting the original variable to latent space, Hotelling T2 statistics is introduced for constraint formulation in latent space. In this way, the constraint is simplified when the optimization is solved in low-dimensional space of latent variable. The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.

Evaluation of ultrasonication as a treatment strategy for enhancement of biohydrogen production from complex distillery wastewater and process optimization

Dark fermentation of distillery wastewater (DWW) gives a lower hydrogen yield (HY) and hydrogen production rate (R), owing to the complexity and a recalcitrant nature of effluent. Therefore, an effective pretreatment of DWW becomes imperative for the improvement of biohydrogen production. In the present study, the efficacy of ultrasonic pretreatment for enhancement of biohydrogen production from DWW was evaluated in batch test. Several variables, such as COD input, ultrasonic density (UD), and ultrasonication time (UT) were studied for optimization using response surface methodology integrated with desirability function. The highest HY, 10.95 mmol/g COD, and R, 6.67 mmol/L h, were obtained for batch test of ultrasonically pretreated DWW under optimal conditions for COD, UD and UT at 56 g/L, 0.12 W/mL, and 17 min, respectively. The significant relative enhancement of HY, 101%, and R, 103%, implies that ultrasonically treated DWW is about 1.2–1.4 times more effective for enhanced biohydrogen production from complex DWW compared to unsonicated DWW.

Load Distribution of Evolutionary Algorithm for Complex-Process Optimization Based on Differential Evolutionary Strategy in Hot Rolling Process

Based on the hot rolling process, a load distribution optimization model is established, which includes rolling force model, thickness distribution model, and temperature model. The rolling force ratio distribution and good strip shape are integrated as two indicators of objective function in the optimization model. Then, the evolutionary algorithm for complex-process optimization (EACOP) is introduced in the following optimization algorithm. Due to its flexible framework structure on search mechanism, the EACOP is improved within differential evolutionary strategy, for better coverage speed and search efficiency. At last, the experimental and simulation result shows that evolutionary algorithm for complex-process optimization based on differential evolutionary strategy (DEACOP) is the organism including local search and global search. The comparison with experience distribution and EACOP shows that DEACOP is able to use fewer adjustable parameters and more efficient population differential strategy during solution searching; meanwhile it still can get feasible mathematical solution for actual load distribution problems in hot rolling process.

Simulation and application of response surface methodology to a nylon‐6 hydrolytic polymerization in a semibatch reactor

AbstractThis work presents an experimental design methodology combined with computational simulation to correlate the influence of operational conditions and reactants charge in the numeric average molecular weight (MWN) as well as on monomer conversion (XCL), for the hydrolytic polymerization of nylon‐6 in a semibatch reactor. It evaluated the reaction temperature, the pressure profile, and the proportion of reactants in the charge. Experimental design was used to screen the most statistically significant variables and to develop a reliable predictive model for each response. The combined use of the models can be applied for process optimization, by establishing MWN and maximum XCL as objective functions. Responses surface allowed the visualization of the responses behavior when changing the independent variables and therefore to identify the optimal tendencies. This work demonstrates that such methodology can be applied for optimization of complex processes like the hydrolytic polymerization of nylon‐6. This polymerization has many side reactions occurring at the same time, which are sensitive to different profiles of pressure and temperature that are applied. This evaluation is quite interesting as such profiles are necessary to perform the several polymerization steps and have a significant impact on product characteristics and therefore in its applications. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013

The optimal process mean problem: Integrating predictability and profitability into an experimental factor space

For complex manufacturing systems, process or product optimization can be instrumental in achieving a significant economic advantage. To reduce costs associated with product non-conformance or excessive waste, engineers often identify the most critical quality characteristics and then use methods to obtain their ideal parameter settings. The optimal process mean problem is one such statistical method; it begins with the assumption of the characteristic parameters, whereby the ideal settings are determined based upon the tradeoff among various processing costs. Unfortunately, however, the ideal parameter settings for a characteristic mean can be unpredictable, as it is directly influenced by changes in the process variability, tolerance, and cost structure. In this paper, a method is proposed that relates the optimal process mean to the ideal settings through experimental design. With the method, one may gain greater predictability of the new optimal process mean when the process conditions are altered. The methodology is illustrated for a process with multiple mixed quality characteristics; such an optimal process mean problem is seldom treated in the literature.