Editor’s Choice Articles

Stirred bioreactors are commonly used unit operations in the pharmaceutical industry. In this study, computational fluid dynamics (CFD) was used in order to analyze the influence of the impeller configuration (Segment–Segment and Segment–Rushton impeller configurations) and the impeller rotational speed (an operational parameter) on the hydrodynamic behaviour and mixing performance of a bioreactor equipped with a double impeller. A relatively close agreement between the power values obtained from the CFD model and those measured experimentally was observed. Various parameters such as velocity profiles, stress generated by impellers due to the turbulence and velocity gradient, flow number, and mixing time were used to compare the CFD simulations. It was observed that the impeller’s RPM could change the intensity of the interaction between the impellers when a Segment–Rushton impeller was used. In general, increasing the RPM led to an increase in total power and the stress acting on the cells and to a shorter mixing time. At a constant RPM, the Segment–Rushton impeller configuration had higher total power and stress acting on cells compared to the Segment–Segment impeller configuration. At lower RPM values (i.e., 50 and 100), the Segment–Segment impeller provided a shorter mixing time. Conversely, at the highest RPM (i.e., 150) the Segment–Rushton impeller had a shorter mixing time compared to the Segment–Segment impeller; this was attributed to the high level of turbulence generated with the former impeller configuration at high RPM. Full article

Bioleaching is an environment-friendly and low-investment process for the extraction of metals from flotation concentrate. Surfactants such as collectors and frothers are widely used in the flotation process. These chemical reagents may have inhibitory effects on the activity of microorganisms through a bioleaching process; however, there is no report indicating influences of reagents on the activity of microorganisms in the mixed culture which is mostly used in the industry. In this investigation, influences of typical flotation frothers (methyl isobutyl carbinol and pine oil) in different concentrations (0.01, 0.10, and 1.00 g/L) were examined on activates of bacteria in the mesophilic mixed culture (Acidithiobacillus ferrooxidans, Leptospirillum ferrooxidans, and Acidithiobacillus thiooxidans). For comparison purposes, experiments were repeated by pure cultures of Acidithiobacillus ferrooxidans and Leptospirillum ferrooxidans in the same conditions. Results indicated that increasing the dosage of frothers has a negative correlation with bacteria activities while the mixed culture showed a lower sensitivity to the toxicity of these frothers in comparison with examined pure cultures. Outcomes showed the toxicity of Pine oil is lower than methyl isobutyl carbinol (MIBC). These results can be used for designing flotation separation procedures and to produce cleaner products for bio extraction of metals. Full article

This paper presents a web-based tool for the optimization of biomass-to-chemicals processing pathways. The tool provides a user-friendly grpahical user interface (GUI) for building a process superstructure, offers the possibility of uploading data from Aspen Plus simulations and generates an optimization code to find the pathway that minimizes the annualized costs or maximizes the net present value. A processing pathway from residues to lactic acid is used to discuss and illustrate the main features of the tool. Full article

Process development, especially in regulated industries, where quality-by-design approaches have become a prerequisite, is cost intensive and time consuming. A main factor is the large number of experiments needed. Process modelling can reduce this number significantly by replacing experiments with simulations. However, this requires a validated model. In this paper, a process and model development workflow is presented, which focuses on implementing, parameterizing, and validating the model in four steps. The presented methods are laid out to gain, create, or generate the maximum information and process knowledge needed for successful process development. This includes design of experiments and statistical evaluations showing process robustness, sensitivity of target values to process parameters, and correlations between process and target values. Two case studies are presented. An ion exchange capture step for monoclonal antibodies focusing on high accuracy and low feed consumption; and one case study for small molecules focusing on rapid process development, emphasizing speed of parameter determination. Full article

Pore network models are powerful tools to simulate invasion and transport processes in porous media. They are widely applied in the field of geology and the drying of porous media, and have recently also received attention in fuel cell applications. Here we want to describe and discuss how pore network models can be used as a prescriptive tool for future water electrolysis technologies. In detail, we suggest in a first approach a pore network model of drainage for the prediction of the oxygen and water invasion process inside the anodic porous transport layer at high current densities. We neglect wetting liquid films and show that, in this situation, numerous isolated liquid clusters develop when oxygen invades the pore network. In the simulation with narrow pore size distribution, the volumetric ratio of the liquid transporting clusters connected between the catalyst layer and the water supply channel is only around 3% of the total liquid volume contained inside the pore network at the moment when the water supply route through the pore network is interrupted; whereas around 40% of the volume is occupied by the continuous gas phase. The majority of liquid clusters are disconnected from the water supply routes through the pore network if liquid films along the walls of the porous transport layer are disregarded. Moreover, these clusters hinder the countercurrent oxygen transport. A higher ratio of liquid transporting clusters was obtained for greater pore size distribution. Based on the results of pore network drainage simulations, we sketch a new route for the extraction of transport parameters from Monte Carlo simulations, incorporating pore scale flow computations and Darcy flow. Full article

Metal–organic frameworks (MOFs) are the porous, crystalline structures made of metal–ligands and organic linkers that have applications in gas storage, gas separation, and catalysis. Several experimental and computational tools have been developed over the past decade to investigate the performance of MOFs for such applications. However, the experimental synthesis of MOFs is still empirical and requires trial and error to produce desired structures, which is due to a limited understanding of the mechanism and factors affecting the crystallization of MOFs. Here, we show for the first time a comprehensive kinetic model coupled with population balance model to elucidate the mechanism of MOF synthesis and to estimate size distribution of MOFs growing in a solution of metal–ligand and organic linker. The oligomerization reactions involving metal–ligand and organic linker produce secondary building units (SBUs), which then aggregate slowly to yield MOFs. The formation of secondary building units (SBUs) and their evolution into MOFs are modeled using detailed kinetic rate equations and population balance equations, respectively. The effect of rate constants, aggregation frequency, the concentration of organic linkers, and concurrent crystallization of organic linkers are studied on the dynamics of SBU and MOF formation. The results qualitatively explain the longer timescales involved in the synthesis of MOF. The fundamental insights gained from modeling and simulation analysis can be used to optimize the operating conditions for a higher yield of MOF crystals. Full article

Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation. Full article

Predictive analytics is usually cited as one of the most important pillars of the digital transformation. For the oil industry, specifically, it is a common belief that issues like integrity and maintenance could benefit from predictive analytics. This paper presents the development and the application of a process-monitoring tool in a real process facility. The PMA (Predictive Maintenance Application) system is a data-driven application that uses a multivariate analysis in order to predict the system behavior. Results show that the use of a multivariate approach for process monitoring could not only detect an early failure at a metering system days before the operation crew, but could also successfully identify, among hundreds of variables, the root cause of the abnormal situation. By applying such an approach, a better performance of the monitored equipment is expected, decreasing its downtime. Full article

Background: The gut microbiota is a heterogeneous group of microbes that is spatially distributed along various sections of the intestines and across the mucosa and lumen in each section. Understanding the dynamics between the spatially differential microbial populations and the driving forces for the observed spatial organization will provide valuable insights into important questions such as the nature of colonization of the infant gut and different types of inflammatory bowel disease localized in different regions of the intestines. However, in most studies, the microbiota is sampled only at a single site (often feces) or from a particular anatomical site of the intestines. Differential oxygen availability is putatively a key factor shaping the spatial organization. Results: To test this hypothesis, we constructed a community genome-scale metabolic model consisting of representative organisms for the major phyla present in the human gut microbiome. By solving step-wise optimization problems embedded in a dynamic framework to predict community metabolism and integrate the mucosally-adherent with the luminal microbiome between consecutive sections along the intestines, we were able to capture (i) the essential features of the spatially differential composition of obligate anaerobes vs. facultative anaerobes and aerobes determined experimentally, and (ii) the accumulation of microbial biomass in the lumen. Sensitivity analysis suggests that the spatial organization depends primarily on the oxygen-per-microbe availability in each region. Oxygen availability is reduced relative to the ~100-fold increase in mucosal microbial density along the intestines, causing the switch between aerobes and anaerobes. Conclusion: The proposed integrated dynamic framework is able to predict spatially differential gut microbiota composition using microbial genome-scale metabolic models and test hypotheses regarding the dynamics of the gut microbiota. It can potentially become a valuable tool for exploring therapeutic strategies for site-specific perturbation of the gut microbiota and the associated metabolic activities. Full article

Thin-film membrane layers coated onto porous supports is widely considered as an efficient way to obtain high-performance oxygen transport membranes with both good permeability and high mechanical strength. However, conventional preparation methods of membrane supports usually result in highly tortuous channels with high mass transfer resistance. Tubular porous MgO and MgO/CGO supports were fabricated with a simple phase inversion casting method. Long finger-like channels were obtained inside the dual-phase supports by adjusting the ceramic loading, polymer concentration and particle surface area, as well as by introducing ethanol inside the casting slurries. Slurries that exhibit lower viscosity in the zero-shear viscosity region resulted in more pronounced channel growth. These supports were used to produce thin supported CGO membranes for possible application in O₂ separation. Similar shrinkage speeds for the different layers during the sintering process are crucial for obtaining dense asymmetric membranes. The shrinkage of the support tube at a high temperature was greatly affected by the polymer/ceramic ratio and compatible shrinkage behaviours of the two layers were realized with polymer/ceramic weight ratios between 0.175 and 0.225. Full article

A data-driven strategy for the online estimation of important kinetic parameters was assessed for the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst at different diene concentrations and reaction temperatures. An initial global sensitivity analysis selected the significant kinetic parameters of the system. The retrospective cost model refinement (RCMR) algorithm was adapted and implemented to estimate the significant kinetic parameters of the model in real time. After verifying stability and robustness, experimental data validated the algorithm performance. Results demonstrate the estimated kinetic parameters converge close to theoretical values without requiring prior knowledge of the polymerization model and the original kinetic values. Full article

Genome-scale models have become indispensable tools for the study of cellular growth. These models have been progressively improving over the past two decades, enabling accurate predictions of metabolic fluxes and key phenotypes under a variety of growth conditions. In this work, an efficient computational method is proposed to incorporate genome-scale models into superstructure optimization settings, introducing them as viable growth models to simulate the cultivation section of biorefinaries. We perform techno-economic and life-cycle analyses of an algal biorefinery with five processing sections to determine optimal processing pathways and technologies. Formulation of this problem results in a mixed-integer nonlinear program, in which the net present value is maximized with respect to mass flowrates and design parameters. We use a genome-scale metabolic model of Chlamydomonas reinhardtii to predict growth rates in the cultivation section. We study algae cultivation in open ponds, in which exchange fluxes of biomass and carbon dioxide are directly determined by the metabolic model. This formulation enables the coupling of flowrates and design parameters, leading to more accurate cultivation productivity estimates with respect to substrate concentration and light intensity. Full article

The synthesis of Zein nanoparticles (NPs) using conventional methods, such as emulsion solvent diffusion and emulsion solvent evaporation, is often unreliable in replicating particle size and polydispersity between batch-to-batch syntheses. We have systematically examined the parameters for reproducibly synthesizing Zein NPs using a Y-junction microfluidics chip with staggered herringbone micromixers. Our results indicate that the total flow rate of the fluidics system, relative flow rate of the aqueous and organic phase, concentration of the base material and solvent, and properties of the solvent influence the polydispersity and size of the NPs. Trends such as increasing the total flow rate and relative flow rate lead to a decrease in Zein NP size, while increasing the ethanol and Zein concentration lead to an increase in Zein NP size. The solvent property that was found to impact the size of the Zein NPs formed the most was their hydropathy. Solvents that had a hydropathy index most similar to that of Zein formed the smallest Zein NPs. Synthesis consistency was confirmed within and between sample batches. Stabilizing agents, such as sodium caseinate, Tween 80, and Pluronic F-68, were incorporated using the microfluidics system, necessary for in vitro and in vivo use, into Zein-based NPs. Full article

Replacing petro-based materials with renewably sourced ones has clearly been applied to polymers, such as those derived from itaconic acid (IA) and its derivatives. Di-n-butyl itaconate (DBI) was (co)polymerized via nitroxide mediated polymerization (NMP) to impart elastomeric (rubber) properties. Homopolymerization of DBI by NMP was not possible, due to a stable adduct being formed. However, DBI/styrene (S) copolymerization by NMP at various initial molar feed compositions f_DBI,0 was polymerizable at different reaction temperatures (70–110 °C) in 1,4 dioxane solution. DBI/S copolymerizations largely obeyed first order kinetics for initial DBI compositions of 10% to 80%. Number-average molecular weight (M_n) versus conversion for various DBI/S copolymerizations however showed significant deviations from the theoretical M_n as a result of chain transfer reactions (that are more likely to occur at high temperatures) and/or the poor reactivity of DBI via an NMP mechanism. In order to suppress possible intramolecular chain transfer reactions, the copolymerization was performed at 70 °C and for a longer time (72 h) with f_DBI,0 = 50%–80%, and some slight improvements regarding the dispersity (Ð = 1.3–1.5), chain activity and conversion (~50%) were observed for the less DBI-rich compositions. The statistical copolymers produced showed a depression in T_g relative to poly(styrene) homopolymer, indicating the effect of DBI incorporation. Full article

Myeloid cell leukemia-1 (Mcl1) is an anti–apoptotic protein that has gained considerable attention due to its overexpression activity prevents cell death. Therefore, a potential inhibitor that specifically targets Mcl1 with higher binding affinity is necessary. Recently, a series of N-substituted 1-hydroxy-4-sulfamoyl-2-naphthoate compounds was reported that targets Mcl1, but its binding mechanism remains unexplored. Here, we attempted to explore the molecular mechanism of binding to Mcl1 using advanced computational approaches: pharmacophore-based 3D-QSAR, docking, and MD simulation. The selected pharmacophore—NNRRR—yielded a statistically significant 3D-QSAR model containing high confidence scores (R² = 0.9209, Q² = 0.8459, and RMSE = 0.3473). The contour maps—comprising hydrogen bond donor, hydrophobic, negative ionic and electron withdrawal effects—from our 3D-QSAR model identified the favorable regions crucial for maximum activity. Furthermore, the external validation of the selected model using enrichment and decoys analysis reveals a high predictive power. Also, the screening capacity of the selected model had scores of 0.94, 0.90, and 8.26 from ROC, AUC, and RIE analysis, respectively. The molecular docking of the highly active compound—C40; 4-(N-benzyl-N-(4-(4-chloro-3,5-dimethylphenoxy) phenyl) sulfamoyl)-1-hydroxy-2-naphthoate—predicted the low-energy conformational pose, and the MD simulation revealed crucial details responsible for the molecular mechanism of binding with Mcl1. Full article

Chitosan films containing citric acid were prepared using a multi-step process called heterogeneous crosslinking. These films were neutralized first, followed by citric acid addition, and then heat treated at 150 °C/0.5 h in order to potentially induce covalent crosslinking. The viscoelastic storage modulus, E′, and tanδ were studied using dynamic mechanical analysis, and compared with neat and neutralized films to elucidate possible crosslinking with citric acid. Films were also prepared with various concentrations of a model crosslinker, glutaraldehyde, both homogeneously and heterogeneously. Based on comparisons of neutralized films with films containing citric acid, and between citric acid films either heat treated or not heat treated, it appeared that the interaction between chitosan and citric acid remained ionic without covalent bond formation. No strong evidence of a glass transition from the tanδ plots was observable, with the possible exception of heterogeneously crosslinked glutaraldehyde films at temperatures above 200 °C. Full article

Diabetic kidney disease (DKD) is a major cause of renal failure. Podocytes are terminally differentiated renal epithelial cells that are key targets of damage due to DKD. Podocytes express a glucose-stimulated local renin-angiotensin system (RAS) that produces angiotensin II (ANG II). Local RAS differs from systemic RAS, which has been studied widely. Hyperglycemia increases the production of ANG II by podocyte cells, leading to podocyte injury. Angiotensin-converting enzyme (ACE) is involved in the production of ANG II, and ACE inhibitors are drugs used to suppress elevated ANG II concentration. As systemic RAS differs from the local RAS in podocytes, ACE inhibitor drugs should act differently in local versus systemic contexts. Experimental and computational studies have considered the pharmacokinetics (PK) and pharmacodynamics (PD) of ACE inhibition of the systemic RAS. Here, a PK/PD model for ACE inhibition is developed for the local RAS in podocytes. The model takes constant or dynamic subject-specific glucose concentration input to predict the ANG II concentration and the corresponding effects of drug doses locally and systemically. The model is developed for normal and impaired renal function in combination with different glucose conditions, thus enabling the study of various pathophysiological conditions. Parameter uncertainty is also analyzed. Such a model can improve the study of the effects of drugs at the cellular level and can aid in development of therapeutic approaches to slow the progression of DKD. Full article

Recent in vitro experiments have demonstrated the ability of the pathogen Clostridium difficile and commensal gut bacteria to form biofilms on surfaces, and biofilm development in vivo is likely. Various studies have reported that 3%–15% of healthy adults are asymptomatically colonized with C. difficile, with commensal species providing resistance against C. difficile pathogenic colonization. C. difficile infection (CDI) is observed at a higher rate in immunocompromised patients previously treated with broad spectrum antibiotics that disrupt the commensal microbiota and reduce competition for available nutrients, resulting in imbalance among commensal species and dysbiosis conducive to C. difficile propagation. To investigate the metabolic interactions of C. difficile with commensal species from the three dominant phyla in the human gut, we developed a multispecies biofilm model by combining genome-scale metabolic reconstructions of C. difficile, Bacteroides thetaiotaomicron from the phylum Bacteroidetes, Faecalibacterium prausnitzii from the phylum Firmicutes, and Escherichia coli from the phylum Proteobacteria. The biofilm model was used to identify gut nutrient conditions that resulted in C. difficile-associated dysbiosis characterized by large increases in C. difficile and E. coli abundances and large decreases in F. prausnitzii abundance. We tuned the model to produce species abundances and short-chain fatty acid levels consistent with available data for healthy individuals. The model predicted that experimentally-observed host-microbiota perturbations resulting in decreased carbohydrate/increased amino acid levels and/or increased primary bile acid levels would induce large increases in C. difficile abundance and decreases in F. prausnitzii abundance. By adding the experimentally-observed perturbation of increased host nitrate secretion, the model also was able to predict increased E. coli abundance associated with C. difficile dysbiosis. In addition to rationalizing known connections between nutrient levels and disease progression, the model generated hypotheses for future testing and has the capability to support the development of new treatment strategies for C. difficile gut infections. Full article

In this paper, a novel pattern-based method is developed for the generation of optimal schedules for energy-integrated batch process systems. The proposed methodology is based on the analysis of available schedules for the identification of repetitive patterns. It is shown that optimal schedules of energy-integrated batch processes are composed of several repeating sections (or building blocks), and their sizes and relative positions are dependent on the scheduling horizon and constraints. Based on such a decomposition, the proposed pattern-based algorithm generates an optimal schedule by computing the number and sequence of these blocks. The framework is then integrated with rigorous optimization-based approach wherein it is shown that the learning from the pattern-based solution significantly improves the performance of rigorous optimization. The main advantage of the pattern-based method is the significant reduction in computational time required to solve large scheduling problems, thus enabling the possibility of on-line rescheduling. Three literature examples were considered to demonstrate the presence of repeating patterns in optimal schedules of energy-integrated batch systems. The effectiveness of the proposed methodology was illustrated using an integrated reactor-separator system. Full article

This study presents the Computational Fluid Dynamics (CFD) thermal design and experimental tests results for a multi-tubular solar reactor for hydrogen production based on the ferrite thermochemical cycle in a pilot plant in the Plataforma Solar de Almería (PSA). The methodology followed for the solar reactor design is described, as well as the experimental tests carried out during the testing campaign and characterization of the reactor. The CFD model developed for the thermal design of the solar reactor has been validated against the experimental measurements, with a temperature error ranging from 1% to around 10% depending on the location within the reactor. The thermal balance in the reactor (cavity and tubes) has been also solved by the CFD model, showing a 7.9% thermal efficiency of the reactor. CFD results also show the percentage of reacting media inside the tubes which achieve the required temperature for the endothermic reaction process, with 90% of the ferrite pellets inside the tubes above the required temperature of 900 °C. The multi-tubular solar reactor designed with aid of CFD modelling and simulations has been built and operated successfully. Full article

Cobalt oxide silica membranes were prepared and tested to separate small molecular gases, such as He (d_k = 2.6 Å) and H₂ (d_k = 2.89 Å), from other gases with larger kinetic diameters, such as CO₂ (d_k = 3.47 Å) and Ar (d_k = 3.41 Å). In view of the amorphous nature of silica membranes, pore sizes are generally distributed in the ultra-microporous range. However, it is difficult to determine the pore size of silica derived membranes by conventional characterization methods, such as N₂ physisorption-desorption or high-resolution electron microscopy. Therefore, this work endeavors to determine the pore size of the membranes based on transport phenomena and computer modelling. This was carried out by using the oscillator model and correlating with experimental results, such as gas permeance (i.e., normalized pressure flux), apparent activation energy for gas permeation. Based on the oscillator model, He and H₂ can diffuse through constrictions narrower than their gas kinetic diameters at high temperatures, and this was possibly due to the high kinetic energy promoted by the increase in external temperature. It was interesting to observe changes in transport phenomena for the cobalt oxide doped membranes exposed to H₂ at high temperatures up to 500 °C. This was attributed to the reduction of cobalt oxide, and this redox effect gave different apparent activation energy. The reduced membrane showed lower apparent activation energy and higher gas permeance than the oxidized membrane, due to the enlargement of pores. These results together with effective medium theory (EMT) suggest that the pore size distribution is changed and the peak of the distribution is slightly shifted to a larger value. Hence, this work showed for the first time that the oscillator model with EMT is a potential tool to determine the pore size of silica derived membranes from experimental gas permeation data. Full article

The market for the small-scale micro gas turbine is expected to grow rapidly in the coming years. Especially, utilization of commercial off-the-shelf components is rapidly reducing the cost of ownership and maintenance, which is paving the way for vast adoption of such units. However, to meet the high-reliability requirements of power generators, there is an acute need of a real-time monitoring system that will be able to detect faults and performance degradation, and thus allow preventive maintenance of these units to decrease downtime. In this paper, a micro gas turbine based combined heat and power system is modelled and used for development of physics-based diagnostic approaches. Different diagnostic schemes for performance monitoring of micro gas turbines are investigated. Full article

Among the vital processes of cutaneous wound healing are epithelialization and angiogenesis. The former leads to the successful closure of the wound while the latter ensures that nutrients are delivered to the wound region during and after healing is completed. These processes are regulated by various cytokines and growth factors that subtend their proliferation and migration into the wound region until full healing is attained. Wound epithelialization can be enhanced by the administration of epidermal stem cells (ESC) or impaired by the presence of an infection. This paper uses the Eden model of a growing cluster to independently simulate the processes of epithelialization and angiogenesis in a cutaneous wound for different geometries. Further, simulations illustrating bacterial infection are provided. Our simulation results demonstrate contraction and closure for any wound geometry due to a collective migration of epidermal cells from the wound edge in fractal form and the diffusion of capillary sprouts with the laying down of capillary blocks behind moving tips into the wound area. Full article

The compressor fuel cost minimization problem (FCMP) for natural gas pipelines is a relevant problem because of the substantial energy consumption of compressor stations transporting the large global demand for natural gas. The common method for modeling the FCMP is to assume key modeling parameters such as the friction factor, compressibility factor, isentropic exponent, and compressor efficiency to be constants, and their nonlinear relationships to the system operating conditions are ignored. Previous work has avoided the complexity associated with the nonlinear relationships inherent in the FCMP to avoid unreasonably long solution times for practical transportation systems. In this paper, a mixed-integer linear programming (MILP) based method is introduced to generate piecewise-linear functions that approximate the previously ignored nonlinear relationships. The MILP determines the optimal break-points and orientation of the linear segments so that approximation error is minimized. A novel FCMP model that includes the piecewise-linear approximations is applied in a case study on three simple gas networks. The case study shows that the novel FCMP model captures the nonlinear relationships with a high degree of accuracy and only marginally increases solution time compared to the common simplified FCMP model. The common simplified model is found to produce solutions with high error and infeasibility when applied on a rigorous simulation. Full article

In this present work, a study of the modelling and simulation for a top-sprayed fluidized bed granulator (SFBG) is presented, which is substantially used by the pharmaceutical industry to prepare granules. The idea is to build a number-based mathematical model using the notion of population balances by dividing a top SFBG into two different zones, namely the wet zone and dry zone. To solve a two-compartment model, an existing accurate and efficient finite volume scheme is implemented. In order to validate the compartmental model, a new class of analytical moments is derived corresponding to various combinations of aggregation and breakage kernels. To verify the accuracy of a modified finite volume scheme, the zeroth and first order moments computed using the finite volume scheme are compared with the newly-derived analytical results. Moreover, the stability of the compartmental model and the numerical scheme is tested by varying the size of the wet zone. It is also shown that the relative errors in both order moments increase with the increase in the size of the wet zone. Full article

Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design. Full article

Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by ∼60%. Results further improve up to $84\%$ when considering on-line optimal experimental design (OED). Our in silico results suggest that MBOED provides a significant advantage in the identification of models of biological parts and should thus be integrated into their characterisation. Full article

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation. Full article

This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several examples of problems that are enabled by the GEKKO library. Full article

Industrialization of bioreactors has been achieved by applying several core concepts of science and engineering. Modeling has deepened the understanding of biological and physical phenomena. In this paper, the state of existing cell culture models is summarized. A framework for development of dynamic and computationally feasible models that capture the interactions of hydrodynamics and cellular activities is proposed. Operating conditions are described by impeller rotation speed, gas sparging flowrate, and liquid fill level. A set of admissible operating states is defined over discretized process parameters. The burden on a dynamic solver is reduced by assuming hydrodynamics at its fully developed state and implementation of compartmental modeling. A change in the conditions of operation is followed by hydrodynamics switching instantaneously to the steady state that would be reached under new conditions. Finally, coupling the model with optimization solvers leads to improvements in operation. Full article

A dynamic model of a concentrating solar thermal array and thermal energy storage system is presented that is differentiable in the design decision variables: solar aperture area and thermal energy storage capacity. The model takes as input the geographic location of the system of interest and the corresponding discrete hourly solar insolation data, and calculates the annual thermal and economic performance of a particular design. The model is formulated for use in determining optimal hybridization strategies for industrial process heat applications using deterministic gradient-based optimization algorithms. Both convex and nonconvex problem formulations are presented. To demonstrate the practicability of the models, they were applied to four different case studies for three disparate geographic locations in the US. The corresponding optimal design problems were solved to global optimality using deterministic gradient-based optimization algorithms. The model and optimization-based analysis provide a rigorous quantitative design and investment decision-making framework for engineering design and project investment workflows. Full article

Physico-chemical modelling and predictive simulation are becoming key for modern process engineering. Rigorous models rely on the separation of different effects (e.g., fluid dynamics, kinetics, mass transfer) by distinct experimental parameter determination on lab-scale. The equations allow the transfer of the lab-scale data to any desired scale, if characteristic numbers like e.g., Reynolds, Péclet, Sherwood, Schmidt remain constant and fluid-dynamics of both scales are known and can be described by the model. A useful model has to be accurate and therefore match the experimental data at different scales and combinations of process and operating parameters. Besides accuracy as one quality attribute for the modelling depth, model precision also has to be evaluated. Model precision is considered as the combination of modelling depth and the influence of experimental errors in model parameter determination on the simulation results. A model is considered appropriate if the deviation of the simulation results is in the same order of magnitude as the reproducibility of the experimental data to be substituted by the simulation. Especially in natural product extraction, the accuracy of the modelling approach can be shown through various studies including different feedstocks and scales, as well as process and operating parameters. Therefore, a statistics-based quantitative method for the assessment of model precision is derived and discussed in detail in this paper to complete the process engineering toolbox. Therefore a systematic workflow including decision criteria is provided. Full article

The spreading of cancer cells, also known as metastasis, is a lethal hallmark in cancer progression and the primary cause of cancer death. Recent cancer research has suggested that the remodeling of collagen fibers in the extracellular matrix (ECM) of the tumor microenvironment facilitates the migration of cancer cells during metastasis. ECM remodeling refers to the following two procedures: the ECM degradation caused by enzyme matrix metalloproteinases and the ECM alignment due to the cross-linking enzyme lysyl oxidase (LOX). Such modifications of ECM collagen fibers result in changes of ECM physical and biomechanical properties that affect cancer cell migration through the ECM. However, the mechanism of such cancer migration through a remodeling ECM remains not well understood. A mathematical model is proposed in this work to better describe and understand cancer migration by means of ECM remodeling. Effects of LOX are considered to enable transport of enzymes and migration of cells through a dynamic, reactive tumor microenvironment that is modulated during cell migration. For validation cases, the results obtained show comparable trends to previously established models. In novel test cases, the model predicts the impact on ECM remodeling and the overall migration of cancer cells due to the inclusion of LOX, which has not yet been included in previous cancer invasion models. Full article

As the pharmaceutical industry seeks more efficient methods for the production of higher value therapeutics, the associated data analysis, data visualization, and predictive modeling require dependable data origination, management, transfer, and integration. As a result, the management and integration of data in a consistent, organized, and reliable manner is a big challenge for the pharmaceutical industry. In this work, an ontological information infrastructure is developed to integrate data within manufacturing plants and analytical laboratories. The ANSI/ISA-88.01 batch control standard has been adapted in this study to deliver a well-defined data structure that will improve the data communication inside the system architecture for continuous processing. All the detailed information of the lab-based experiment and process manufacturing, including equipment, samples and parameters, are documented in the recipe. This recipe model is implemented into a process control system (PCS), data historian, as well as Electronic Laboratory Notebook (ELN) system. Data existing in the recipe can be eventually exported from this system to cloud storage, which could provide a reliable and consistent data source for data visualization, data analysis, or process modeling. Full article

This work investigates the effect of various membrane substrates and coating conditions on the formation of carbon/ceramic mixed matrix membranes for desalination application. The substrates were impregnated with phenolic resin via a vacuum-assisted method followed by carbonization under an inert gas. Substrates with pore sizes of 100 nm required a single impregnation step only, where short vacuum times (<120 s) resulted in low quality membranes with defects. For vacuum times of ≥120 s, high quality membranes with homogeneous impregnation were prepared leading to high salt rejection (>90%) and high water fluxes (up to 25 L m⁻² h⁻¹). The increase in water flux as a function of the vacuum time confirms the vacuum etching effect resulting from the vacuum-assisted method. Substrates with pore sizes of 140 nm required two impregnation steps. These pores were too large for the ceramic inter-particle space to be filled with phenolic resin via a single step. In the second impregnation step, increasing the concentration of the phenolic resin resulted in membranes with lower water fluxes. These results indicate that thicker films were formed by increasing the phenolic resin concentration. In the case of substrates with pores of 600 nm, these pores were too large and inter-particle space filling with phenolic resin was not attained. Full article

RNA editing by RNA specific adenosine deaminase acting on RNA (ADAR) is increasingly being found to alter microRNA (miRNA) regulation. Editing of miRNA transcripts can affect their processing, as well as which messenger RNAs (mRNAs) they target. Further, editing of target mRNAs can also affect their complementarity to miRNAs. Notably, ADAR editing is often increased in malignancy with the effect of these RNA changes being largely unclear. In addition, numerous reports have now identified an array of miRNAs that directly contribute to various malignancies although the majority of their targets remain largely undefined. Here we propose that modulating the targets of miRNAs via mRNA editing is a frequent occurrence in cancer and an underappreciated participant in pathology. In order to more accurately characterize the relationship between these two regulatory processes, this study examined RNA editing events within mRNA sequences of two breast cancer cell lines (MCF-7 and MDA-MB-231) and determined whether or not these edits could modulate miRNA associations. Computational analyses of RNA-Seq data from these two cell lines identified over 50,000 recurrent editing sites within human mRNAs, and many of these were located in 3′ untranslated regions (UTRs). When these locations were screened against the list of currently-annotated miRNAs we discovered that editing caused a subset (~9%) to have significant alterations to mRNA complementarity. One miRNA in particular, miR-140-3p, is known to be misexpressed in many breast cancers, and we found that mRNA editing allowed this miRNA to directly target the apoptosis inducing gene DFFA in MCF-7, but not in MDA-MB-231 cells. As these two cell lines are known to have distinct characteristics in terms of morphology, invasiveness and physiological responses, we hypothesized that the differential RNA editing of DFFA in these two cell lines could contribute to their phenotypic differences. Indeed, we confirmed through western blotting that inhibiting miR-140-3p increases expression of the DFFA protein product in MCF-7, but not MDA-MB-231, and further that inhibition of miR-140-3p also increases cellular growth in MCF-7, but not MDA-MB-231. Broadly, these results suggest that the creation of miRNA targets may be an underappreciated function of ADAR and may help further elucidate the role of RNA editing in tumor pathogenicity. Full article

Fuel gas network (FGN) synthesis is a systematic method for reducing fresh fuel consumption in a chemical plant. In this work, we address FGN synthesis problems using a block superstructure representation that was originally proposed for process design and intensification. The blocks interact with each other through direct flows that connect a block with its adjacent blocks and through jump flows that connect a block with all nonadjacent blocks. The blocks with external feed streams are viewed as fuel sources and the blocks with product streams are regarded as fuel sinks. An additional layer of blocks are added as pools when there exists intermediate operations among source and sink blocks. These blocks can be arranged in a $I\times$ J two-dimensional grid with $I=1$ for problems without pools, or $I=2$ for problems with pools. J is determined by the maximum number of pools/sinks. With this representation, we formulate FGN synthesis problem as a mixed-integer nonlinear (MINLP) formulation to optimally design a fuel gas network with minimal total annual cost. We revisit a literature case study on LNG plants to demonstrate the capability of the proposed approach. Full article

This paper discusses the techno-economic assessment of hydrogen production from biogas with conventional systems. The work is part of the European project BIONICO, whose purpose is to develop and test a membrane reactor (MR) for hydrogen production from biogas. Within the BIONICO project, steam reforming (SR) and autothermal reforming (ATR), have been identified as well-known technologies for hydrogen production from biogas. Two biogases were examined: one produced by landfill and the other one by anaerobic digester. The purification unit required in the conventional plants has been studied and modeled in detail, using Aspen Adsorption. A pressure swing adsorption system (PSA) with two and four beds and a vacuum PSA (VPSA) made of four beds are compared. VPSA operates at sub-atmospheric pressure, thus increasing the recovery: results of the simulations show that the performances strongly depend on the design choices and on the gas feeding the purification unit. The best purity and recovery values were obtained with the VPSA system, which achieves a recovery between 50% and 60% at a vacuum pressure of 0.1 bar and a hydrogen purity of 99.999%. The SR and ATR plants were designed in Aspen Plus, integrating the studied VPSA model, and analyzing the behavior of the systems at the variation of the pressure and the type of input biogas. The SR system achieves a maximum efficiency, calculated on the LHV, of 52% at 12 bar, while the ATR of 28% at 18 bar. The economic analysis determined a hydrogen production cost of around 5 €/kg of hydrogen for the SR case. Full article

Emulsion polymerization process provides a unique polymerization locus that has a confined tiny space with a higher polymer concentration, compared with the corresponding bulk polymerization, especially for the ab initio emulsion polymerization. Assuming the ideal polymerization kinetics and a constant polymer/monomer ratio, the effect of such a unique reaction environment is explored for both conventional and living free-radical polymerization (FRP), which involves chain transfer to the polymer, forming polymers with long-chain branches. Monte Carlo simulation is applied to investigate detailed branched polymer architecture, including the mean-square radius of gyration of each polymer molecule, <s²>₀. The conventional FRP shows a very broad molecular weight distribution (MWD), with the high molecular weight region conforming to the power law distribution. The MWD is much broader than the random branched polymers, having the same primary chain length distribution. The expected <s²>₀ for a given MW is much smaller than the random branched polymers. On the other hand, the living FRP shows a much narrower MWD compared with the corresponding random branched polymers. Interestingly, the expected <s²>₀ for a given MW is essentially the same as that for the random branched polymers. Emulsion polymerization process affects branched polymer architecture quite differently for the conventional and living FRP. Full article

Studies performed at single-cell resolution have demonstrated the physiological significance of cell-to-cell variability. Various types of mathematical models and systems analyses of biological networks have further been used to gain a better understanding of the sources and regulatory mechanisms of such variability. In this work, we present a novel sensitivity analysis method, called molecular density function perturbation (MDFP), for the dynamical analysis of cellular heterogeneity. The proposed analysis is based on introducing perturbations to the density or distribution function of the cellular state variables at specific time points, and quantifying how such perturbations affect the state distribution at later time points. We applied the MDFP analysis to a model of a signal transduction pathway involving TRAIL (tumor necrosis factor-related apoptosis-inducing ligand)-induced apoptosis in HeLa cells. The MDFP analysis shows that caspase-8 activation regulates the timing of the switch-like increase of cPARP (cleaved poly(ADP-ribose) polymerase), an indicator of apoptosis. Meanwhile, the cell-to-cell variability in the commitment to apoptosis depends on mitochondrial outer membrane permeabilization (MOMP) and events following MOMP, including the release of Smac (second mitochondria-derived activator of caspases) and cytochrome c from mitochondria, the inhibition of XIAP (X-linked inhibitor of apoptosis) by Smac, and the formation of the apoptosome. Full article

The error-in-variables-model (EVM) is the most statistically correct non-linear parameter estimation technique for reactivity ratio estimation. However, many polymer researchers are unaware of the advantages of EVM and therefore still choose to use rather erroneous or approximate methods. The procedure is straightforward but it is often avoided because it is seen as mathematically and computationally intensive. Therefore, the goal of this work is to make EVM more accessible to all researchers through a series of focused case studies. All analyses employ a MATLAB-based computational package for copolymerization reactivity ratio estimation. The basis of the package is previous work in our group over many years. This version is an improvement, as it ensures wider compatibility and enhanced flexibility with respect to copolymerization parameter estimation scenarios that can be considered. Full article

Lignocellulose is an abundant and renewable resource that holds great promise for sustainable bioprocessing. However, unpretreated lignocellulose is recalcitrant to direct utilization by most microbes. Current methods to overcome this barrier include expensive pretreatment steps to liberate cellulose and hemicellulose from lignin. Anaerobic gut fungi possess complex cellulolytic machinery specifically evolved to decompose crude lignocellulose, but they are not yet genetically tractable and have not been employed in industrial bioprocesses. Here, we aim to exploit the biomass-degrading abilities of anaerobic fungi by pairing them with another organism that can convert the fermentable sugars generated from hydrolysis into bioproducts. By combining experiments measuring the amount of excess fermentable sugars released by the fungal enzymes acting on crude lignocellulose, and a novel dynamic flux balance analysis algorithm, we screened potential consortia partners by qualitative suitability. Microbial growth simulations reveal that the fungus Anaeromyces robustus is most suited to pair with either the bacterium Clostridia ljungdahlii or the methanogen Methanosarcina barkeri—both organisms also found in the rumen microbiome. By capitalizing on simulations to screen six alternative organisms, valuable experimental time is saved towards identifying stable consortium members. This approach is also readily generalizable to larger systems and allows one to rationally select partner microbes for formation of stable consortia with non-model microbes like anaerobic fungi. Full article

This experimental and theoretical study deals with the thermal spontaneous polymerization of n-butyl acrylate (n-BA). The polymerization was carried out in solution (n-heptane as the solvent) at 200 and 220 °C without adding any conventional initiators. It was studied with the five different n-BA/n-heptane volume ratios: 50/50, 70/30, 80/20, 90/10, and 100/0. Extensive experimental data presented here show significant monomer conversion at all temperatures and concentrations confirming the occurrence of the thermal self-initiation of the monomer. The order, frequency factor, and activation energy of the thermal self-initiation reaction of n-BA were estimated from n-BA conversion, using a macroscopic mechanistic model. The estimated reaction order agrees well with the order obtained via our quantum chemical calculations. Furthermore, the frequency factor and activation energy estimates agree well with the corresponding values that we already reported for bulk polymerization of n-BA. Full article

The radical copolymerization kinetics of acrylamide (AM) and the water-soluble monomer sodium 4-hydroxy-4-methyl-2-methylene butanoate (SHMeMB), formed by saponification of the bio-sourced monomer γ-methyl-α-methylene-γ-butyrolactone (MeMBL), are investigated to explain the previously reported slow rates of reaction during synthesis of superabsorbent hydrogels. Limiting conversions were observed to decrease with increased temperature during SHMeMB homopolymerization, suggesting that polymerization rate is limited by depropagation. Comonomer composition drift also increased with temperature, with more AM incorporated into the copolymer due to SHMeMB depropagation. Using previous estimates for the SHMeMB propagation rate coefficient, the conversion profiles were used to estimate rate coefficients for depropagation and termination (k_t). The estimate for k_t,SHMeMB was found to be of the same order of magnitude as that recently reported for sodium methacrylate, with the averaged copolymerization termination rate coefficient dominated by the presence of SHMeMB in the system. In addition, it was found that depropagation still controlled the SHMeMB polymerization rate at elevated temperatures in the presence of added salt. Full article

Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA), which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based on the singular value decomposition achieved the best results in the majority of test cases involving process datasets. Full article

The realization of physiologically-relevant advanced in vitro models is not just related to the reproduction of a three-dimensional multicellular architecture, but also to the maintenance of a cell culture environment in which parameters, such as temperature, pH, and hydrostatic pressure are finely controlled. Tunable and reproducible culture conditions are crucial for the study of environment-sensitive cells, and can also be used for mimicking pathophysiological conditions related with alterations of temperature, pressure and pH. Here, we present the SUITE (Supervising Unit for In Vitro Testing) system, a platform able to monitor and adjust local environmental variables in dynamic cell culture experiments. The physical core of the control system is a mixing chamber, which can be connected to different bioreactors and acts as a media reservoir equipped with a pH meter and pressure sensors. The chamber is heated by external resistive elements and the temperature is controlled using a thermistor. A purpose-built electronic control unit gathers all data from the sensors and controls the pH and hydrostatic pressure by regulating air and CO₂ overpressure and flux. The system’s modularity and the possibility of imposing different pressure conditions were used to implement a model of portal hypertension with both endothelial and hepatic cells. The results show that the SUITE platform is able to control and maintain cell culture parameters at fixed values that represent either physiological or pathological conditions. Thus, it represents a fundamental tool for the design of biomimetic in vitro models, with applications in disease modelling or toxicity testing. Full article

The process of oxygenic photosynthesis is robust and ubiquitous, relying centrally on input of light, carbon dioxide, and water, which in many environments are all abundantly available, and from which are produced, principally, oxygen and reduced organic carbon. However, photosynthetic machinery can be conflicted by the simultaneous presence of carbon dioxide and oxygen through a process sometimes called photorespiration. We present here a model of phototrophy, including competition for RuBisCO binding sites between oxygen and carbon dioxide, in a chemostat-based microbial population. The model connects to the idea of metabolic pathways to track carbon and degree of reduction through the system. We find decomposition of kinetics into elementary flux modes a mathematically natural way to study synchronization of mismatched rates of photon input and chemostat turnover. In the single species case, though total biomass is reduced by photorespiration, protection from excess light exposures and its consequences (oxidative and redox stress) may result. We also find the possibility that a consortium of phototrophs with heterotrophs can recycle photorespiration byproduct into increased biomass at the cost of increase in oxidative product (here, oxygen). Full article

The design of new drug delivery systems has been widely sought after. The stability, solubility, and difficulty of targeting active sites for new drugs have always been challenging and remain one of the major drawbacks to the efficiency of certain drugs. Liposomes are phospholipid vesicles enclosing one or more aqueous compartments. Depending on its properties, a drug is embedded in the lipid bilayer or the aqueous medium. Thus, liposomes can act as drug carriers for both lipo- and hydrophilic compounds. New strategies such as “drug-in-cyclodextrin-in liposomes” (DCLs) have been developed as safe and effective carriers for exploiting the inclusion properties of water-soluble cyclodextrins known to form host–guest complexes with lipophilic molecules. Once inclusion complexes are formed, they can be inserted into a liposome aqueous core in order to stabilize it and better control the drug release. Our review will provide an update on the use of DCLs in the field of drug delivery for various kinds of active compounds. While previous reviews focused on the interesting advantages of using this method, such as enhancing the solubility and stability of a drug or controlling and improving drug release, the authors intend to highlight the impact of these nanocarriers on the pharmacokinetic and/or pharmacodynamic properties of drugs. Full article

Dry machining has gained significant importance in the last few years due to its promising contribution towards sustainability. This review study introduces dry machining, presents its benefits, and summarizes the recent technological developments that can facilitate dry machining. It aims to provide a concise overview of the current state of the art in dry machining to promote sustainability. This article synthesizes and emphasizes the useful information from the existing literature, and summarizes the methods and tools used to implement it. It also identifies some of the major problems and challenges and their potential solutions to make dry machining more viable and efficient. It concludes with some future research directions important for the scholars and researchers to establish the field further. From this review study, the major findings are: (1) tools with textures or patterns can enhance the cutting performance of dry machining for various materials, (2) tool coating is an effective way to lower the tool cost in dry machining and can achieve the required functionality for the cutting tool without affecting its core properties, (3) Alumina-based mixed ceramic tools with SiC whiskers have better fracture toughness, thermal shock resistance, and self-crack healing properties, (4) one effective method to improve the dry cutting of engineering materials is to apply external energy sources to assist the dry machining process, (5) by using microwave sintering, cutting tools with finer microstructures and higher densities can be produced, which improve their hardness, wear resistance, and thermal stability to perform well in dry machining conditions. Full article

The critical role of energy in contemporary life and the environmental challenges associated with its production imply the need for research and exploration of its novel resources. The present review paper emphasizes the continuous exploitation of non-renewable energy sources, suggesting the transition toward renewable energy sources, termed ‘green energy’, as a crucial step for sustainable development. The research methodology involves a comprehensive review of articles, statistical data analysis, and examination of databases. The main focus is biomass, a valuable resource for bioenergy and biopesticide production, highlighting not only its traditional diverse sources, such as agricultural waste and industrial residues, but also non-edible invasive alien plant species. This study explores the utilization of invasive alien species in circular economy practices, considering their role in bioenergy and biopesticide production. The potential conflict between bioproduct acquisition and food sector competition is discussed, along with the need for a shift in approaching non-edible biomass sources. The paper emphasizes the untapped potential of under-explored biomass resources and the necessity for policy alignment and public awareness. Species with a significant potential for these sustainable strategies include Acer negundo L., Ailanthus altisima (Mill.) Swingle., Amorpha fruticosa L., Elaengus angustifolia L., Falopia japonica (Houtt.) Ronse Decr., Hibiscus syriacus L., Koelreuteria paniculata Laxm., Paulownia tomentosa Siebold and Zucc., Partenocissus quenquefolia (L.) Planch., Rhus typhina L., Robinia pseudoacacia L. and Thuja orientalis L. In conclusion, the paper highlights the intertwined relationship between energy, environmental sustainability, and circular economy principles, providing insights into Serbia’s efforts and potential in adopting nature-based solutions for bioenergy and biopesticides acquisition. Full article