Product Quality Constraints Research Articles

Glass formulation and lab-scale testing of glasses designed for in-can Melter and in-container Vitrification of high-assay low-enriched uranium aqueous polishing Raffinate waste

Glasses were designed for processing a nuclear waste from aqueous polishing of high-assay low-enriched uranium using either In-Can Melter or GeoMelt® In-Container Vitrification™ (ICV) technologies, which operate at temperatures of Tp ≤ 1100 °C and Tp ≤ 1450 °C, respectively. Due to the different operating conditions, the melt and glass properties were optimized differently for each technology. Each glass was designed to optimize for maximum waste loading while simultaneously satisfying processing (e.g., crystallization, viscosity, conductivity) and product quality (e.g., durability, hazard characteristic, crystal content) constraints. The raffinate waste contains high nitric acid (4 M) and low total solids (9 g∙L−1) concentrations. Feed preparation processes were tested to facilitate concentration and nitrate destruction/removal while controlling redox of the melter feed. A successful lab-scale feed process including sugar addition and spray-drying was performed to generate an adequate melter feed for ICV processing.

Digital design of an integrated purification system for continuous pharmaceutical manufacturing

In this work dynamic models of the continuous crystallization, filtration, deliquoring, washing, and drying steps are introduced, which are developed in the open-source pharmaceutical modeling tool PharmaPy. These models enable the simulation and digital design of an integrated continuous two-stage crystallization and filtration-drying carousel system. The carousel offers an intensified process that can manufacture products with tailored properties through optimal design and control. Results show that improved crystallization design enhances overall process efficiency by improving critical material attributes of the crystal slurry for downstream filtration and drying operations. The digital design of the integrated process achieves enhanced productivity while satisfying multiple design and product quality constraints. Additionally, the impact of model uncertainty on the optimal operating conditions is investigated. The findings demonstrate the systematic process development potential of PharmaPy, providing improved process understanding, design space identification, and optimized robust operation.

Hybrid model development for parameter estimation and process optimization of hydrophobic interaction chromatography

Hydrophobic Interaction Chromatography (HIC) is often employed as a polishing step to remove aggregates for the purification of therapeutic proteins in the biopharmaceutical industry. To accelerate the process development and save the costs of performing time- and resource-intensive experiments, advanced model-based process design and optimization are necessary. Due to the unclear adsorption mechanism of the salt-dependent interaction between the protein and resin, the development of an accurate mechanistic model to describe the complex HIC behavior is challenging. In this work, an isotherm derived from Wang et al. is modified by adding three extra parameters together with an equilibrium dispersive model to represent the HIC process. To reduce the development effort of isotherm equations and extract missing information from the available data, a hybrid model is constructed by combining a simple and well-known multi-component Langmuir isotherm (MCL) with a neural network (NN). It is observed that the structure of the hybrid model is of critical importance to the accuracy of the developed model. During parameter estimation, a regularization strategy is incorporated to prevent overfitting. Furthermore, the impact of NN structures and regularization rates are comprehensively investigated. One of the interesting findings was that a simple NN with one hidden layer with two nodes and sigmoid as the activation function, significantly outperforms the mechanistic model, with a 62% improvement in accuracy in calibration and 31.4% in validation. To ensure the generalizability of the developed hybrid model, an in-silico dataset is generated using the mechanistic model to test the extrapolation capability of the hybrid model. Process optimization is also carried out to find the optimal operating conditions under product quality constraints using the developed hybrid model.

Experimental Application of Real-Time Optimization with Modifier Adaptation and Quadratic Approximation to a Reactive Extrusion Process

In this contribution, Real-Time Optimization with Modifier Adaptation and Quadratic Approximation (MAWQA) is experimentally applied to a pilot-scale 18 mm reactive extruder for the production of hydrophobically modified ethoxylated urethanes which are used as rheological paint additives. Standard model-based control is in this application not applicable because of the complex coupling of the chemical reaction, complex internal flows within the equipment, and a high sensitivity to external disturbances. The optimization is performed with the goal to minimize the specific energy requirement for the product by changing the overall throughput and the barrel temperature locally while meeting a hard constraint on the product quality. The product quality is determined by the viscosity of the product which is measured online with a capillary viscometer. In contrast to other work, the MAWQA method is applied here purely data-based without using a physical model. Despite the absence of a rigorous model, the method converged to the optimum after 6 initial probing moves and 4 iterations where the product quality constraints were already met.

Multiperiod refinery optimization for mitigating the impact of process unit shutdowns

Process unit shutdowns, whether planned or unplanned, disrupt the normal operation of process plants. The negative impact of such disruptions can often be mitigated by the strategic use of material inventories, coupled with redistribution of material flows. In this paper, an optimization framework is proposed for a multiperiod refinery planning model in which a system of inventory tanks is used to mitigate the impact of process unit shutdowns. An economic objective is optimized over a planning horizon, subject to operating, design, and product quality constraints. Two formulation paradigms are considered - an operating problem in which optimal material flow trajectories are determined in response to a shutdown scenario, and a retrofit design problem in which the optimal location and sizing of inventory tanks are determined, the latter formulated as a two-stage stochastic programming problem. The utility of the formulation to shutdown planning is shown through case studies of the operational and design problems applied to a simple refinery scheme.

Dynamic Optimization Study for Cryogenic Distillation in Hydrogen Isotope Separation System

This paper presents a dynamic optimization study on a cryogenic distillation unit in hydrogen isotope separation system. A packed distillation column with an equilibrator is considered. The packed column was modeled as a theoretical equilibrium-based stage column. The equilibrator was placed to enhance separation performance. An optimal control problem was formulated to minimize the tritium holdup under two product quality constraints, in a situation where feed flowrate is periodically fluctuated. An optimal control policy was obtained for two manipulating variables, namely distillate flowrate and reboiler heat duty. The optimal control policy was analyzed by examining the operating variable profiles of the column.

Dynamic process intensification: Fundamentals and implementation to ternary distillation

AbstractDistillation remains a key technology for separating liquid mixtures. Its versatility comes with the disadvantage of high energy consumption. We previously used empirical arguments to introduce dynamic process intensification (DPI) as a strategy for improving the energy efficiency of binary distillation. In this article, we focus on ternary distillation; we begin by providing a rigorous basis for DPI, then formulate the problem of identifying the operating states for DPI as a nonlinear optimization problem. Via an extensive case study considering a hydrocarbon mixture, we demonstrate that reboiler energy use can be reduced by more than 2.3% relative to an equivalent column operating at steady state. We prove that this result is due to the fact that DPI aims to satisfy product flow and quality constraints on average in time, affording broader opportunities for optimizing column economics than steady‐state operation, where constraints are met strictly at all times.

Integration of Scheduling and Control Under Stochastic Parametric Uncertainty with Varying Unit Operation Times for Chemical Batch Plants: A Back-Off Approach

Abstract A new back-off methodology is presented in this work as an approach for solving MIDO formulations arising for the optimal scheduling and control of flow-shop batch plants under stochastic parametric uncertainty. The proposed algorithm decomposes the MIDO problem into a scheduling problem, a dynamic optimization problem and a unit time operation minimization problem. These problems are solved iteratively using back-off terms. Parametric uncertainty is modeled using statistical distribution functions and are embedded in the algorithm to ensure dynamic feasibility of the optimal control actions under stochastic realizations in those parameters. The proposed algorithm identifies scheduling and control decisions offline. To exemplify this methodology, the integration of scheduling and control of a flow-shop batch plant is considered. The results show that unit operation times chosen from optimization are better suited to accommodate stochastic parametric uncertainty while the control actions enforce process operational and product quality constraints at reasonable costs.

The impacts of financing constraints on the quality upgrading of China's agri-food exporters

PurposeThe purpose of this paper is to identify the impacts of financing constraints on the quality upgrading of China's agri-food sector.Design/methodology/approachOur empirical study is performed using the “distance to the frontier” framework. We employ a merged sample of Chinese agri-food trading firms based on Chinese firm-level data from the National Bureau of Statistics of China and Chinese customs data. To verify robustness, we test whether the results hold when using different definitions of quality upgrading and alternative proxy variables for product quality and financing constraints. To examine the heterogeneous effects, we generate subsamples by firm location, export destination and the product sophistication of exports.FindingsThe results suggest that financing constraints have a significant negative impact on the product quality upgrading of Chinese agri-food export firms. In addition, the negative impacts of financing constraints are more severe for firms close to the quality frontier than for those far from the frontier. These results are robust to various checks. Moreover, the heterogeneous effects of financing constraints on quality upgrading are identified when the sample is split according to firm location, export destination and the sophistication of export products.Originality/valueThis paper reviews and applies some recent studies in the literature to investigate the relationship between financing constraints and the product quality upgrading of agri-food export firms in China. Overall, the results of this paper could be considered of importance for promoting the quality upgrading of export products in the China's agri-food sector.

Integration of semi-batch cultivation and extraction for maximal lipid production in Chlamydomonas sp. Tai-03

Rapid improvements in bioseparation technology, new regulatory directives, product quality constraints, and the production efficiency have necessitated the development of more advanced and powerful downstream bioprocesses for biotechnology and biopharmaceuticals industrial. This has transformed in dramatically improvements in traditional bioseparation processes as well as the development of entirely new approaches. In this paper, we highlight some of these recent advances of integration of semi-batch cultivation and extraction for maximal lipid production in Chlamydomonas sp. Tai-03. This includes extractive cultivation, extractive bioconversion aqueous two-phase system, aqueous two-phase flotation, and newly developed liquid biphasic flotation. Alcohol/salt liquid biphasic flotation (LBF) with aid of ultrasonication which have the ability of killing two birds with one stone, it not only capable in cell rupturing, it also able to recover bioproducts simultaneously and continuously. The effect of varying crude feedstock concentration, flotation time, type of salt, concentration of salt, type of alcohol, concentration of alcohol, initial volumes of salt and alcohol were investigated. Microalgal biofuels or generation three biofuels have been widely recognized as potential replacements of fossil fuels. One of the most attractive option is the partial or full replacement of diesel fuel with microalgal biodiesel. Here, Chlamydomonas sp. Tai-03 was cultured using semi-batch cultivation to enhance its lipid production. Upon lowering the culture replacement fraction to 25%, the greatest biomass and lipid productivities were obtained at 1.23 ± 0.02 g/L/d and 239.6 ± 24.8 mg/L/d. After transesterification, palmitic acid (C16:0), oleic acid (C18:1), and linoleic acid (C18:2) were the main fatty acid methyl esters (FAMEs) present. These short-chain FAMEs and high productivities of Chlamydomonas sp. Tai-03 are suitable for biodiesel output.

Variable export price elasticity, product quality, and credit constraints: Theory and evidence from Greek firms

We propose a simple model with endogenous quality, additive non-homothetic preferences and credit constraints. A unique data set of Greek manufacturing firms with firm-level exports, credit scores and other financial variables supports the model’s main predictions. Specifically, we establish that less credit-constrained Greek exporters with higher credit scores face export demand curves with lower price elasticity, charge higher export prices, and export higher-quality products.

Multiobjective Dynamic Optimization of Ampicillin Batch Crystallization: Sensitivity Analysis of Attainable Performance vs Product Quality Constraints

Ampicillin is a broad-spectrum antibiotic and World Health Organization Essential Medicine whose crystallization is an important unit operation in its production. A published model for the solubili...

Model-based energy performance analysis of high pressure processing systems

Abstract Mathematical models are developed to simulate the behaviour of high pressure processing systems (single- and multi-cycle processes of different pulse-shapes) and predict the effects of processing parameters on their energy consumption. The validity of the models is established by comparing simulation results with experimental measurements from published works and the present study. Specific energy consumption is shown to depend mainly on holding pressure, pressure medium compressibility, equipment scale and vessel filling efficiency. Inlet temperature, compression and decompression times show negligible effects as do cycle pressure shapes. Longer compression times, however, reduce power capacity requirements, if all other conditions remain constant. The holding time has negligible effects on energy consumption, save for leakages and standby power, hence, extending it does not incur significant energy penalties. On the other hand, a drop in holding pressure leads to a more than proportionate drop in energy consumption. Hence, lower-pressure, longer-time processes are more advantageous from an energy standpoint, provided they satisfy product quality, safety and throughput requirements. Lower-compressibility fluids enable higher pressures to be established with lower energy losses. Higher equipment scales and vessel filling efficiencies reduce the proportion of wasted energy. These conditions are therefore beneficial for energy-efficient operation. Industrial relevance The production of clean-label, minimally-processed and microbial-safe food products with excellent nutritional, organoleptic properties and extended shelf life is becoming increasingly important. High-pressure processing HPP is a promising technology in this regard, increasingly being deployed at commercial scale. To reduce per-unit HPP product costs, which are currently higher than those of traditional thermal systems, it is important to reduce energy usage, which constitutes a significant proportion of operating costs. The modelling scheme developed in this work would help process designers and operators determine optimal processing conditions with respect to energy consumption, while satisfying product quality and safety constraints; providing a basis for improved process automation.

Hierarchical MPC of batch reactors with shared resources

Multi-reactor semi-batch plants are widespread in the chemical and pharmaceutical industry, since they can produce more flexibly and depending on the product also more profitably than continuous plants. Such reactors usually share resources that are constrained, as for instance raw materials or cooling and heating media. In order to maximize the productivity while respecting product quality constraints, equipment limitations, as well as constraints on the utilization of the shared resources, the feeding policies and temperature profiles in such semi-batch reactors are increasingly optimized online. This can be done by a plant-wide optimizing controller, however, due to robustness, flexibility, and reduced computational effort, distributed schemes that solve the combined trajectory optimization and resource assignment problem are of high interest. In this contribution, we present a hierarchical model predictive control scheme that computes cost and resource optimal trajectories online for a set of semi-batch reactors. Each reactor maximizes its profit function locally by maximizing the amount of product while incurring a cost for the use of the shared resources. On the coordinator level, the future availability of the shared resources is taken into account and the prices are adjusted iteratively, such that the feasibility of the joint operation of all reactors is guaranteed.We show that the problem can be solved using the alternating direction method of multipliers (ADMM), modified for inequality constraints, and compare the performance of this scheme to a decentralized one. Furthermore, the need to coordinate over the whole prediction horizon is discussed and a reduced coordination horizon and its selection are investigated.

Subspace Identification-Based Modeling and Control of Batch Particulate Processes

This paper addresses the problem of subspace-based model identification and predictive control of particulate process subject to uncertainty and time-varying parameters. To this end, subspace identification techniques are first adapted to handle the batch nature of the data. A linear model predictive controller (MPC) is next formulated to enable achieving a particle size distribution with desired characteristics subject to both manipulated input and product quality constraints. The proposed approach is implemented on a seeded batch crystallizer process and compared with an open-loop policy, as well as a traditional trajectory tracking policy, using classical control. The proposed MPC is shown to achieve superior performance and the ability to respect tighter product quality constraints, as well as robustness to uncertainty.

ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment

The ever-increasing distributed, networked and crowd-sourced cloud environment imposes the need of a service-oriented product data model for explicit representation of service requests in global manufacturing-service networks. The work in this paper aims to develop such a description framework for products based on semantic web technologies to facilitate the make-to-individual production strategy in a cloud manufacturing environment. A brief discussion on the requirements of a product data model in cloud manufacturing and research on product data modelling is given in the first part. A systematic ontology development methodology is then proposed and elaborated. The ontology called ManuService has been developed, consisting of all necessary concepts for description of products in a service-oriented business environment. These concepts include product specifications, quality constraints, manufacturing processes, organisation information, cost expectations, logistics requirements, and etcetera. ManuService ontology provides a module-based, reconfigurable, privacy-enhanced and standardised approach to modelling customised manufacturing service requests. An industrial case is presented to demonstrate possible applications using ManuService ontology. Comprehensive discussions are given thereafter, including a pilot application of a software package for semantic-based product design and a semantic web-based module for intelligent knowledge-based decision-making based on ManuService. ManuService forms the basis for collaborative service-oriented business interactions, intelligent and secure service provision in cloud manufacturing environment.

Moving Horizon Scheduling of an Air Separation Unit under Fast-Changing Energy Prices

Maximizing the benefits of time-of-use pricing for industrial electricity consumers requires varying production rates, such that energy use is shifted from peak price periods to off-peak times during the day. Assuming that excess capacity and product storage are available, production of energy intensive processes can be increased at off-peak times beyond nominal rates, and the stored product can be used at peak times when the production rate is lowered. Under these rapidly changing circumstances, scheduling calculations must take into consideration explicitly the dynamic model of the process, often rendering the scheduling problem intractable in practical amounts of time. To address this challenge, we introduce a class of scheduling-relevant low-order process models, which capture the closed-loop input-output behavior of a plant. We use these models to close the scheduling loop, whereby the scheduling problem is formulated over a moving horizon with feedback. We apply the theoretical concepts to an industrial-scale air separation unit model, demonstrating that variable production rate operation with product storage has the potential for significant operating cost savings while abiding by product quality and safety constraints.

Hybrid optimization and simulation to design a logistics network for distributing perishable products

Dynamics in product quality complicate the design of logistics networks for perishable products, like flowers and other agricultural products. Complications especially arise when multiple products from different origins have to come together for processes like bundling. This paper presents a new MILP model and a hybrid optimization–simulation (HOS) approach to identify a cost-optimal network design (i.e. facility location with flow and process allocation) under product quality requirements. The MILP model includes constraints on approximated product quality. A discrete event simulation checks the feasibility of the design that results from the MILP assuming uncertainties in supply, processing and transport. Feedback on product quality from the simulation is used to iteratively update the product quality constraints in the MILP. The HOS approach combines the strengths of strategic optimization via MILP and operational product quality evaluation via simulation. Results, for various network structures and varying degrees of dynamics and uncertainty, show that if quality decay is not taken into account in the optimization, low quality products are delivered to the final customer, which results in not meeting service levels and excess waste. Furthermore, case results show the effectiveness of the HOS approach, especially when the change from one iteration to the next is in the choice of locations rather than in the number of location. It is shown that the convergence of the HOS approach depends on the gap between the product quality requirements and the quality that can be delivered according to the simulation.

Exogenous Expenses in Industries with Vertical Product Differentiation and Quality Constraints

In this paper we study how an exogenous expense of owning a market good affects the equilibrium outcome in a market with vertical product differentiation i.e. consumers differ by income but have identical preferences for the good’s quality. We identify three possible subgame-perfect equilibrium outcomes dependent on the amount of the exogenous expense. First, at a small exogenous expense tending to zero, quality choice is characterized by maximal product differentiation and all consumers buy one of the two qualities in the market. Second, at a medium exogenous expense, some low-income consumers refrain from buying which incentivizes the producer of the low-quality good to minimize its difference from the high-quality good. In turn, it chooses the best quality from its individually constrained set of quality choices. Third, at a large exogenous expense at which the consumers of the low-quality good cannot afford it, the market is monopolized by the high-quality firm.

A multivariate statistical framework for the analysis of software effort phase distribution

ContextIn software project management, the distribution of resources to various project activities is one of the most challenging problems since it affects team productivity, product quality and project constraints related to budget and scheduling. ObjectiveThe study aims to (a) reveal the high complexity of modelling the effort usage proportion in different phases as well as the divergence from various rules-of-thumb in related literature, and (b) present a systematic data analysis framework, able to offer better interpretations and visualisation of the effort distributed in specific phases. MethodThe basis for the proposed multivariate statistical framework is Compositional Data Analysis, a methodology appropriate for proportions, along with other methods like the deviation from rules-of-thumb, the cluster analysis and the analysis of variance. The effort allocations to phases, as reported in around 1500 software projects of the ISBSG R11 repository, were transformed to vectors of proportions of the total effort and were analysed with respect to prime project attributes. ResultsThe proposed statistical framework was able to detect high dispersion among data, distribution inequality and various interesting correlations and trends, groupings and outliers, especially with respect to other categorical and continuous project attributes. Only a very small number of projects were found close to the rules-of-thumb from the related literature. Significant differences in the proportion of effort spent in different phrases for different types of projects were found. ConclusionThere is no simple model for the effort allocated to phases of software projects. The data from previous projects can provide valuable information regarding the distribution of the effort for various types of projects, through analysis with multivariate statistical methodologies. The proposed statistical framework is generic and can be easily applied in a similar sense to any dataset containing effort allocation to phases.