Freeze-drying is generally used to achieve high quality products and preserve thermal sensitive components; however, it is also considered as a high energy and costly process. Modeling of the process can help to optimize the process to reduce these drawbacks. In this work, a mathematical model is presented to predict the heat and mass transfer behavior for freeze-drying of porous frozen food particles during freeze-drying to optimize the process. For the mass transfer, a comparison between Knudsen diffusion and the more complex dusty-gas approach is performed. Simulation results of a single particle are validated by experiments of single-layer drying to extend the usage of this model from a single particle to a particle bed. For the moisture transfer, adaption parameters are introduced and evaluated. A comparison shows a good agreement of the model with experimental results. The results furthermore suggest a strong correlation of the drying kinetics with pore size and particle porosity... [more]

In this paper, we investigate the problem of species separation in minimal time. Droop model is considered to describe the evolution of two distinct populations of microorganisms that are in competition for the same resource in a photobioreactor. We focus on an optimal control problem (OCP) subject to a five-dimensional controlled system in which the control represents the dilution rate of the chemostat. The objective is to select the desired species in minimal-time and to synthesize an optimal feedback control. This is a very challenging issue, since we are are dealing with a ten-dimensional optimality system. We provide properties of optimal controls allowing the strain of interest to dominate the population. Our analysis is based on the Pontryagin Maximum Principle (PMP), along with a thorough study of singular arcs that is crucial in the synthesis of optimal controls. These theoretical results are also extensively illustrated and validated using a direct method in optimal control (... [more]

Market globalisation, shortened patent lifetimes and the ongoing shift towards personalised medicines exert unprecedented pressure on the pharmaceutical industry. In the push for continuous pharmaceutical manufacturing, processes need to be shown to be agile and robust enough to handle variations with respect to product demands and operating conditions. In this paper we examine the use of operational envelopes to study the trade-off between the design and operational flexibility of the fluid bed dryer at the heart of a tablet manufacturing process. The operating flexibility of this unit is key to the flexibility of the full process and its supply chain. The methodology shows that for the fluid bed dryer case study there is significant effect on flexibility of the process at different drying times with the optimal obtained at 700 s. The flexibility is not affected by the change in volumetric flowrate, but only by the change in temperature. Here the method used a black box model to show... [more]

The zigzag (ZZ) classifier is a sorting and classification device with a wide range of applications (e.g. recycling, food industry). Due to the possible variation of geometry and process settings, the apparatus is used for various windows of operation due to the specifications of the separation (e.g. cut sizes from 100 µm to several decimetres, compact and fluffy materials as well as foils). Since the ZZ classifier gains more and more interest in recycling applications, it is discussed in this paper, with regards to its design, mode of operation, influencing parameters and the research to date. Research on the ZZ-classifier has been ongoing on for more than 50 years and can be divided into mainly experimental studies and modelling approaches.

Process chromatography modelling for process development, design, and optimization as well as process control has been under development for decades. Still, the discussion of scientific potential and industrial applications needs is open to innovation. The discussion of next-generation modelling approaches starting from Langmuirian to steric mass action and multilayer or thermodynamic consistent real and ideal adsorption theory or colloidal particle adsorption approaches is continued.

This study aimed to analyze and quantify the effect of different ratios of vermicompost, phosphate rock, and sulfur on P solubilization and release by Pseudomonas fluorescens Ur21, and to identify optimal levels of those variables for an efficient biofertilizer. Twenty experiments were defined by surface response methodology based on a central composite design (CCD), and the effects of various quantities of vermicompost, phosphate rock, and sulfur (encoded by −1, 0, or +1) on P solubilization was explored. The results show that the CCD model had high efficiency for predicting P solubilization (R2 = 0.9035). The strongest effects of the included variables on the observed P solubilization were linear effects of sulfur and organic matter (vermicompost), a quadratic effect of phosphate rock, and an interactive effect of organic matter × phosphate rock. Statistical analysis of the coefficients in the CCD model revealed that vermicompost, vermicompost × phosphate rock, and phosphate rock × p... [more]

Chromatography is a technique widely used in the purification of biopharmaceuticals, and generally consists of several chromatographic steps. In this work, Hydrophobic Interaction Chromatography (HIC) is investigated as a polishing step for the purification of therapeutic proteins. Adsorption mechanisms in hydrophobic interaction chromatography are still not completely clear and a limited amount of published data is available. In addition to new data on adsorption isotherms for some proteins (obtained both by high-throughput and frontal analysis method), and a comparison of different models proposed in the literature, two different approaches are compared in this work to investigate HIC. The predictive approach exploits an in-house code that simulates the behavior of the component in the column using the model parameters found from the fitting of experimental data. The estimation approach, on the other hand, exploits commercial software in which the model parameters are found by the fi... [more]

Biosorption has great potential in removing toxic effluents from wastewater, especially heavy metal ions such as cobalt, lead, copper, mercury, cadmium, nickel and other ions. Mathematically modeling of biosorption process is essential for the economical and robust design of equipment employing the bioadsorption process. However, biosorption is a complex physicochemical process involving various transport and equilibrium processes, such as absorption, adsorption, ion exchange and surface and interfacial phenomena. The biosorption process becomes even more complex in cases of multicomponent systems and needs an extensive parametric analysis to develop a mathematical model in order to quantify metal ion recovery and the performance of the process. The biosorption process involves various process parameters, such as concentration, contact time, pH, charge, porosity, pore size, available sites, velocity and coefficients, related to activity, diffusion and dispersion. In this review paper,... [more]

Robots and robotic systems have become an inevitable part of modern industrial settings. Robotics systems are being introduced for various household services as well. As the interactions between the workspace of robots and humans increases, there is an increased likelihood of unintended harm being caused by the robots to humans due to collisions or abrupt contact. To mitigate this, active and passive compliant mechanisms must be introduced in these systems. In this study, a design optimization case study is carried out for the optimization of a passive compliance mechanism achieved with variable stiffness joints realized by the use of permanent magnets. Three design parameters of the systems, namely, inner stator width, outer stator width, and magnet height, are considered. The objective is to minimize the weight and maximize the maximum torque. A nature-inspired metaheuristic hybridized with a multi-criteria decision-making method is introduced to achieve this. The Non-dominated Sorti... [more]

The effect of the partition coefficient on the simulation of the operation of a biotrickling filter treating a mixture of sulfur compounds was analyzed to evaluate the pertinence of using Henry’s law in determining its removal capacity. The analysis consisted of the simulation of a biotrickling filter that bio-oxides hydrogen sulfide (H2S), dimethyl sulfide (DMS), methyl mercaptan (MM) and dimethyl disulfide (DMDS) using different types of models for determining the partition coefficient: Henry’s law for pure water, Henry’s law adjusted from experimental data, a mixed model (Extended UNIQUAC) and a semi-empirical model of two-parameters. The simulations were compared with experimental data. It was observed that Henry’s law for pure water could produce significant deviations from empirical data due to the liquid phase not being pure water. The two-parameter model better fits with similar results compared to the extended UNIQUAC model, with a lower calculation cost and necessary paramete... [more]

To build a DEM model of Cyperus esculentus seed particles, the shape and size of the Cyperus esculentus seed particles were measured and analyzed. The results showed that the dispersity in size had a normal distribution. Additionally, a certain functional relationship between the primary dimension and secondary dimensions was determined. The width of the seed was the primary dimension, and the other secondary dimensions (length and thickness) were calculated based on their relationships with the primary dimension. On this basis, an approach for modeling Cyperus esculentus seed particles based on the multi-sphere (MS) method was proposed. The discrete element analysis models of three varieties of Cyperus esculentus seeds were established with different numbers of filing spheres. Moreover, to obtain more accurate simulation parameters, first, a range of values of the simulation parameters was obtained by the experimental method. Second, the Plackett−Burman (PB) test and the path of steep... [more]

Burgeoning swarm intelligence techniques have been creating a feasible theoretical computational method for the modeling, simulation, and optimization of complex systems. This study aims to increase the coverage of a wireless sensor network (WSN) and puts forward an enhanced version of the sparrow search algorithm (SSA) as a processing tool to achieve this optimization. The enhancement of the algorithm covers three aspects. Firstly, the Latin hypercube sampling technique is utilized to generate the initial population to obtain a more uniform distribution in the search space. Secondly, a sine cosine algorithm with adaptive adjustment and the Lévy flight strategy are introduced as new optimization equations to enhance the convergence efficiency of the algorithm. Finally, to optimize the individuals with poor fitness in the population, a novel mutation disturbance mechanism is introduced at the end of each iteration. Through numerical tests of 13 benchmark functions, the experimental resu... [more]

The current study aims to explore the potential of the combination of hyperspectral imaging and chemometrics in the rapid identification of four chicken breeds. The hyperspectral data of four chicken breeds were collected in the range of 400−900 nm. Five pretreatment methods were used to pretreat the original spectra. The important characteristic wavelength variables were extracted by random frog (RF), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS) algorithms. The classification models were established by using support vector machine (SVM), k-nearest neighbor (KNN), and partial least squares-discriminant analysis (PLS-DA). The results showed that the mean normalization pretreatment method was preferable, and overall classification accuracy of SVM-based models was higher than that of KNN-based and PLS-DA-based models. The correct classification rate (CCR) of the full-spectrum SVM model (Full-SVM) could reach 96.25%. The SPA method extracted 13... [more]

The type of solid substrate plays a critical role in determining the kinetics of the spray fluidized bed (SFB) agglomeration process. In the case of porous (also soft) primary particles (PPs), droplet aging is due to imbibition and drying. The surface properties of the substrate also change due to imbibition. The focus of the present work is to simulate the agglomeration of the spray-dried milk powder using the Monte Carlo (MC) method coupled with a drying-imbibition model. In order to extract the morphology of the formed agglomerates, an aggregation model is employed. Further, this aggregation model is employed to predict the number of positions on the PPs (later agglomerates) for droplet deposition; previously, the ‘concept of positions’ was used. The transient growth of different milk powders (whole and skim) is depicted using the enhanced MC model. The enhancement in the droplet deposition model had a prominent influence on the overall kinetics of agglomeration. As expected, this e... [more]

Anthropogenic climate change, global warming, environmental pollution, and fossil fuel depletion have been identified as critical current scenarios and future challenges. Cement plants are one of the most impressive zones, emitting 15% of the worldwide contaminations into the environment among various industries. These contaminants adversely affect human well-being, flora, and fauna. Meanwhile, the use of cement-based substances in various fields, such as civil engineering, medical applications, etc., is inevitable due to the continuous increment of population and urbanization. To cope with this challenge, numerous filtering methods, recycling techniques, and modeling approaches have been introduced. Among the various statistical, mathematical, and computational modeling solutions, Python has received tremendous attention because of the benefit of smart libraries, heterogeneous data integration, and meta-models. The Python-based models are able to optimize the raw material contents and... [more]

In 2019, a new lethal and mutant virus (COVID-19) spread around the world, causing the deaths of millions of people. COVID-19 demonstrates that scientists are involved in significant research efforts to face bacteria with less effort than that dedicated to viruses. Since then, engineers and bio-materials scientists have been trying to develop antiviral research and find a suitable effective medication. Strategies and opportunities for interference diagnostics, treatment strategies, and predicting future factors became mandatory. From a statistical point of view, estimating and modelling these factors play an important role in preventing future viral epidemics. In this article, modelling the recovery rate of COVID-19 is investigated through a new distribution which is called the unit exponential Pareto distribution. The new continuous distribution with three parameters displays a prominent level of flexibility to model decreasing, symmetric, and asymmetric data with a monotone failure r... [more]

Gas−liquid flow is a significant phenomenon in various engineering applications, such as in nuclear reactors, power plants, chemical industries, and petroleum industries. The prediction of the flow patterns is of great importance for designing and analyzing the operations of two-phase pipeline systems. The traditional numerical and empirical methods that have been used for the prediction are known to result in a high inaccuracy for scale-up processes. That is why various artificial intelligence-based (AI-based) methodologies are being applied, at present, to predict the gas−liquid flow regimes. We focused in the current study on a thorough comparative analysis of machine learning (ML) and deep learning (DL) in predicting the flow regimes with the application of a diverse set of ML and DL frameworks to a database comprising 11,837 data points, which were collected from thirteen independent experiments. During the pre-processing, the big data analysis was performed to analyze the correla... [more]

Investigations were undertaken to study the drying kinetics of pretreated and unblanched leaves of Moringa oleifera dried in a fluidized bed dryer (FBD) using nine established thin layer drying mathematical models. The statistical software tool Statistica was utilized to carry out regression analysis, and the model constants were evaluated using nonlinear regression. In nonlinear regression, the R2 and reduced χ2 were employed to evaluate the goodness of fit of several mathematical models to the data generated experimentally. The model with the highest R2 and the lowest reduced χ2 and root mean square error (RMSE) values was adjudged as best fit to the drying curves. The drying kinetics of drumstick leaves was best explained by the Midilli−Kucuk model, followed by the Logarithmic model. The R2, reduced χ2, and RMSE values of the Midilli−Kucuk model under fluidized bed drying varied from 0.9982−0.9997, 0.00003−0.00029, and 0.0059−0.0166 in pretreated and 0.9945−0.9961, 0.00019−0.00054 a... [more]

Bioscorodite crystallization is a promising process for the proper immobilization of arsenic from acidic metallurgical wastewater, and Acidianus brierleyi is an effective archaeon to oxidize Fe(II) and As(III) simultaneously. This paper deals with the development of an experimentally validated mathematical model to gain insight into the simultaneous processes of Fe(II) and As(III) oxidation via microbial cells and the exopolysaccharide (EPS) matrix, As(V) precipitation, and bioscorodite crystallization, which are affected by several factors. After the mathematical structure was proposed, a model fitting was performed, finding global determination coefficients between 0.96 and 0.99 (with p-values < 0.001) for all the variables. The global sensitivity analysis via Monte Carlo simulations allowed us to identify the critical parameters whose sensitivity depends on culture conditions. The model was then implemented to evaluate the effect of cell concentration, Fe(II) and As(III) concentr... [more]

The research aims to produce, model, and optimise the mechanical properties of novel composite material through a structured multidisciplinary approach. The primary objective is to combine materials science, mechanical engineering, and statistical concepts to ensure Design for Manufacturability (DFM) from the industrial perspective. More specifically, the article is intended to determine the optimal mixture components and predictive model of Al-Si alloy with Al2O3 by accommodating multi-responses that enable DFM. The study adopted ASTM standards to prepare and test the novel composite material. Additionally, the Mixture Design of Experiment (DOE) approach was used to design the experimentation and subsequent analysis. In addition, microstructural images, Cox Response Trace plot, and Response Optimiser plot are effectively utilised to draw robust inferences. For multi-response modelling and optimisation, the composite material’s mechanical properties, like impact strength, hardness, den... [more]

The separation of polyethylene glycols (PEGs) into single homologs by reversed-phase chromatography is investigated experimentally and theoretically. The used core−shell column is shown to achieve the baseline separation of PEG homologs up to molar weights of at least 5000 g/mol. A detailed study is performed elucidating the role of the operating conditions, including the temperature, eluent composition, and degree of polymerization of the polymer. Applying Martin’s rule yields a simple model for retention times that holds for a wide range of conditions. In combination with relations for column efficiency, the role of the operating conditions is discussed, and separations are predicted for analytical-scale chromatography. Finally, the approach is included in an efficient process model based on discrete convolution, which is demonstrated to predict with high accuracy also advanced operating modes with arbitrary injection profiles.

Example Aspen Plus chemical process simulations used in the book Exergy Tables: A Comprehensive Set of Exergy Values to Streamline Energy Efficiency Analysis, by Lingyan Deng, Thomas A. Adams II, and Truls Gundersen (McGraw-Hill Education, 2023). The examples are:

1. Medium-pressure steam generation using a natural-gas powered boiler
2. Medium-pressure steam generation using a natural-gas powered boiler with an economizer
3. Medium-pressure steam generation using an off-gas powered boiler
4. Postcombustion CO2 capture using diglycolamine (DGA) with CCS

Note, stream conditions may vary slightly from those in the book when simulated with different versions of the software.

Files are Aspen Plus v12.1, but should be openable on any version 12.1 or later.

SimaPro model used in this work.

This study aimed to determine the effects of osmotic dehydration on the kinetics of hot air drying of apricot halves under conditions that were similar to the industrial ones. The osmotic process was performed in a sucrose solution at 40 and 60 °C and concentrations of 50% and 65%. As expected increased temperatures and concentrations of the solution resulted in increased water loss, solid gain and shrinkage. The kinetics of osmotic dehydration were well described by the Peleg model. The effective diffusivity of water 5.50−7.387 × 10−9 m2/s and solute 8.315 × 10−10−1.113 × 10−9 m2/s was calculated for osmotic dehydration. Hot air drying was carried out at 40, 50, and 60 °C with air flow velocities of 1.0 m/s and 1.5 m/s. The drying time shortened with higher temperature and air velocity. The calculated effective diffusion of water was from 3.002 × 10−10 m2/s to 1.970 × 10−9 m2/s. The activation energy was sensitive to selected air temperatures, so greater air velocity resulted in great... [more]

Models and codes that were used in this work. Please read the simulation instruction.