Key Process Characteristics Research Articles

Novel Catalytic Reactive Distillation Processes for a Sustainable Chemical Industry

Reactive distillation (RD) is a great process intensification concept taking advantage of the synergy created when combining (catalyzed) reaction and separation into a single unit, which allows the concurrent production and removal of products. This feat improves the productivity and selectivity, reduces the energy usage, eliminates the need for solvents, and leads to highly-efficient systems with improved sustainability metrics (e.g. less waste and emissions). This paper provides an overview of the key features of RD processes, with emphasis on novel catalytic/reactive distillation processes that can make a difference at large scale and pave the way for a more sustainable chemical process industry that is more profitable, safer and less polluting. These examples include the production of: acrylic and methacrylic monomers, unsaturated polyesters resins, di-alkyl ethers, fatty esters, as well as other short alkyl esters (e.g. by enzymatic reactive distillation). The main drivers for such new RD applications are: economical (large reduction of costs and energy use), environmental (lower CO2 emissions, no or reduced waste) and social (improved safety and health due to lower reactive content, reduced footprint and run away sensitivity). Hence RD technology strongly contributes to all three pillars of sustainability in the chemical process industry. Nonetheless, the potential of RD technology has not been fully tapped yet, and there is still undergoing research to improve it further by various means: e.g. ultrasound or microwave assisted RD, use of high-gravity fields (HiGee), internally heat integration, cyclic operation, or coupling RD with other operations such as membrane separations.

Rare behavior of growth processes via umbrella sampling of trajectories.

We compute probability distributions of trajectory observables for reversible and irreversible growth processes. These results reveal a correspondence between reversible and irreversible processes, at particular points in parameter space, in terms of their typical and atypical trajectories. Thus key features of growth processes can be insensitive to the precise form of the rate constants used to generate them, recalling the insensitivity to microscopic details of certain equilibrium behavior. We obtained these results using a sampling method, inspired by the "s-ensemble" large-deviation formalism, that amounts to umbrella sampling in trajectory space. The method is a simple variant of existing approaches, and applies to ensembles of trajectories controlled by the total number of events. It can be used to determine large-deviation rate functions for trajectory observables in or out of equilibrium.

Assembly of Lyotropic Liquid Crystals with Solid Crystal's Structural Order Translated from the Lipid Rafts in Cell Membranes.

Self-assembly offers a powerful way to control the complexity and hierarchy of nanoscale materials, and promises to create a diverse range of emergent properties. Successful syntheses that allow a delicate structural design of building units play an important role. However, as can be learned from many cellular processes and functions, coself-assembly using logically chosen additives should be equally effective in designing self-assembly. Herein I show that, translated from the dynamic nanoscale assemblies in cell membranes known as lipid rafts, coself-assembly of 1-decanol into cetyltrimethylammonium chloride micelles for the assembly of lyotropic liquid crystals generates new structural complexity and hierarchy, and a surprising property that is emerging from it. Designing the intermolecular forces in the way that cholesterol interacts with sphingolipids promotes the synergistic balance between the flexibility and rigidity, and the unique molecular recognition for silicic acid, followed by the micelle coalescence. This very much resembles the assembly process of the lipid rafts in cell membranes and triggers orders of magnitude of sharp increases in X-ray diffraction intensity. The analysis of the diffraction patterns shows that the structural order of these liquid crystals matches that of solid crystals, often of single crystals. Furthermore, the assembly of the liquid crystals promotes a substantial increase in the condensation rate of silicic acids by guiding them to form a silicate trimer along the surface of micelles. This very much resembles the role of the lipid rafts that sharply increases the reaction rate of biomolecules by guiding them to form discrete species along the surface of membranes. This finding demonstrates that it is possible to translate the key features of cellular processes and functions into artificial self-assembling systems of our choice using the building units that are readily available, thus creating novel soft materials.

Sociometrics and observational assessment of teaming and leadership in a cyber security defense competition

Advancing our understanding about the human dynamics of cyber security is a major research challenge. At this point, it is unclear how cyber defense teams are organized and led in coordinating and working together to mount and conduct an effective cyber defense. Therefore, we do not know what makes a cyber defense team more or less effective in responding to and mitigating cyber attacks. Cyber competitions offer an approach to train and evaluate the performance of cyber defense teams; such competitions are now regularly conducted at the high school, college, professional, and military defense levels. These naturalistic exercises of teamwork for cyber defense represent an important source for understanding the way defense teams form, coordinate and organize, and to determine the factors that make teams more or less successful. For this purpose, we participated in data collection at the Mid-Atlantic Collegiate Cyber Defense Competition (MACCDC) to understand the key features of effective team processes defined by outcome measures of scored team success. We collected data from wearable social sensors to assess face-to-face interactions and using a 16-point teamwork instrument called OAT (Observational Assessment of Teamwork) to assess teamwork and leadership behaviors in cyber defense. Importantly, this being a cyber defense competition, the success of these teams is evaluated along three independent scoring dimensions: (a) Maintaining Services, (b) Incidence Response, and (c) Scenario Injects. Our results indicate that the leadership dimension and face-to-face interactions are important factors that determine the success of these teams. Teams with effective leadership were more successful, and face-to-face interactions emerged as a strong negative predictor of success. Thus, functional specialization within a team and well-guided leadership could be important predictors of timely detection and mitigation of ongoing cyber attacks. Future research should address more concrete aspects of team coordination and evaluation in cyber defense teams.

Identification of key design characteristics for complex product adaptive design

Key design characteristics (KDCs) are important information related to the product and part designs, which significantly influence on the product’s functions, performances, and quality. Identifying KDCs for a complex product will help designers to focus on key design parameters during the design process and rapidly obtain design schemes based on their close relationships to the product’s functions, performances, and quality. Although there are some researches on key characteristic (KC) identification, most of them are focused on key process characteristics (KPCs) and few on KDCs. There also lacks a KDC identification framework to support KDC identification with better completeness and diverse usages. Adaptive design is the most important pattern of complex product design. Therefore, this paper presents a systematic method to identify KDCs for complex product adaptive design, in which KDCs can be determined by two related phases. Firstly, a product design specification (PDS)-KDC Candidates Network (PKCN) is constructed by using existing product instance data, cluster analysis, KC flow-down, and network analysis approaches. Then, the result from the first phase is used as a basis to identify KDCs for adaptive design. Three KDC identification techniques: similarity reasoning technique, breadth-first search (BFS), and the gray relational analysis approach are applied to find out KDCs from the PKCN, which are the most sensitive to the variation of a PDS. These identified KDCs can help designers to understand the relationships between KDCs and PDS and rapidly develop a design scheme. The effectiveness and the feasibility of the proposed method are verified by a case study via the development of an electric multiple unit (EMU)’s bogie.

Przyszła polityka spójności UE - wyzwania

A debate on the shape of European Cohesion Policy (ECP) post-2020 has already started in the European Union. It is being waged amid unfavorable conditions, both internal and external. The global economic crisis that erupted after 2007 had a particularly strong effect on the EU, especially its weak and peripheral regions. In addition, Europe has faced a refugee crisis, an increased terrorism threat and anti-integration trends. All this poses a dramatic challenge to the future of the European Union, including ECP. The purpose of this paper is to answer the question of how to configure this sphere of the European Union’s structural intervention and adjust it to changing needs under adverse external and internal conditions. The paper describes the key features of ECP and real processes in the EU’s territorial systems. It also indicates the need to strengthen the territorial dimension of ECP. As ECP requires continued improvement, the paper presents an analysis and recommendations for greater creativity and flexibility of ECP, combined with necessary simplifications and increased effectiveness. Further on, the paper describes how ECP is financed in terms of its share of the EU budget, fund allocation criteria and the territorial scope of EU structural intervention. The complementarity and logic of interventions under individual European Structural and Investment Funds are analyzed. The final section of the paper presents recommendations on how cohesion policy should be shaped after 2020.

Petroleum production optimization – A static or dynamic problem?

This paper considers the upstream oil and gas domain, or more precisely the daily production optimization problem in which production engineers aim to utilize the production systems as efficiently as possible by for instance maximizing the revenue stream. This is done by adjusting control inputs like choke valves, artificial lift parameters and routing of well streams. It is well known that the daily production optimization problem is well suited for mathematical optimization. The contribution of this paper is a discussion on appropriate formulations, in particular the use of static models vs. dynamic models. We argue that many important problems can indeed be solved by repetitive use of static models while some problems, in particular related to shale gas systems, require dynamic models to capture key process characteristics. The reason for this is how reservoir dynamics interacts with the dynamics of the production system.

Enzymatic Hydrolysis of Catfish (Pangasius hypophthalmus) By-Product: Kinetic Analysis of Key Process Parameters and Characteristics of the Hydrolysates Obtained

ABSTRACTIn this study, kinetic evaluation of enzymatic hydrolysis of catfish (Pangasius hypophthalmus) by-product meat was conducted. The effects of key factors such as type of proteases (neutrase, papain, and bromelain), enzyme to substrate ratio (E/S), substrate concentration, and extent of hydrolysis on process efficiency, properties and functionalities of hydrolysates were evaluated. The results demonstrated that, for catfish shredded meat, bromelain and papain had higher affinity than neutrase. Kinetics parameters Vm and Km of neutrase, papain, and bromelain were 4.486, 9.060, and 18.249 μM/min and 35.433, 6.582, and 7.034 g/l, respectively. Optimal E/S ratios of neutrase, papain, and bromelain were estimated to be 0.92, 2.76, and 3.31 mg/g. At optimal E/S, the highest possible substrate concentrations were found to be 108.4 g/l for neutrase, 36.2 g/l for papain, and 135.8 g/l for bromelain. Both kinetics study and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis showed that bromelain and papain had higher proteolytic activity toward catfish protein than neutrase. A significant increase in antioxidant activity of hydrolysates was observed with neutrase and papain hydrolysate, but not with bromelain. The results could be helpful for industrial processors to develop an efficient process for catfish by-product valorization.

Preparation of the HIV Attachment Inhibitor BMS-663068. Part 9. Active Pharmaceutical Ingredient Process Development and Powder Properties

This final communication, of a nine part publication series, details the process development history for the final synthetic step to prepare the drug substance BMS-663068 tris(hydroxymethyl)aminomethane (TRIS) salt. The challenge of developing a robust commercial process to prepare BMS-663068-TRIS salt (active pharmaceutical ingredient, API) was achieved by studying the underlying mechanisms that governed key processing characteristics. Eliminating a slurry-to-slurry transformation results in predictable reaction kinetics and control of impurity formation. Key powder property aspects, such as specific surface area and bulk density, were controlled by examining the impact of seed age, crystallization relative supersaturation (RSS), and particle attrition due to agitation during drying. Ultimately, the processing parameters established for preparation of this drug substance resulted in the generation of the target compound with consistent quality, powder properties, and yield across multiple batches.

Splicing factors act as genetic modulators of TDP-43 production in a new autoregulatory TDP-43 Drosophila model.

TDP-43 is a critical RNA-binding factor associated with RNA metabolism. In the physiological state, maintaining normal TDP-43 protein levels is critical for proper physiological functions of the cells. As such, TDP-43 expression is tightly regulated through an autoregulatory negative feedback loop. TDP-43 is a major disease-causing protein in Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Lobar Degeneration (FTLD). Several studies argue for a pathogenic role of elevated TDP-43 levels in these disorders. Modulating the cycle of TDP-43 production might therefore provide a new therapeutic strategy. In this study, we developed a new transgenic Drosophila model mimicking the TDP-43 autoregulatory feedback loop in order to identify genetic modulators of TDP-43 protein steady-state levels in vivo. First, we showed that our TDP-43_TDPBR Drosophila model recapitulates key features of the TDP-43 autoregulatory processes previously described in mammalian and cellular models, namely alternative splicing events, differential usage of polyadenylation sites, nuclear retention of the transcript and a decrease in steady-state mRNA levels. Using this new Drosophila model, we identified several splicing factors, including SF2, Rbp1 and Sf3b1, as genetic modulators of TDP-43 production. Interestingly, our data indicate that these three RNA-binding proteins regulate TDP-43 protein production, at least in part, by controlling mRNA steady-state levels.

A Variable-Correlation-Based Sparse Modeling Method for Industrial Process Monitoring

Dimensionality reduction techniques are widely used in data-driven process monitoring methods for extracting key process features from process data. Dimensionality reduction generally leads to information loss, and therefore may reduce the process monitoring performance. However, choosing an appropriate data projection matrix that can minimize the effect of dimensionality reduction on process monitoring performance is often challenging. In this paper, we introduce a method to construct a variable-correlation-based sparse projection matrix (VCBSPM) for reducing the dimension of process data and for building the process monitoring model. The VCBSPM is constructed on the basis of variable correlations, with each column of VCBSPM corresponding to a variable group consisting of highly correlated variables. The VCBSPM has two advantages: (1) it implements dimensionality reduction only for highly correlated variables, and therefore the negative effect of dimensionality reduction on process monitoring performance...

(Invited) Alkaline Fuel Cells (AFCs) and Alkaline Membrane Fuel Cells ( AMFCs)

I was fortunate to have many technical discussions with Russ Kunz over the years and learnt a lot from him. Russ was a rarely encountered combination of scientist and technologist , a combination which is prerequisite for being a leader, as he was, in the field of fuel cell development. I chose the subject of this talk having been a common theme in our technical discussions, as Russ was the house expert at UTC for the alkaline fuel cells (AFCs) used in space applications, whereas I got intrigued in recent years by the potential of alkaline membrane fuel cells ( AMFCs) which share key features with the AFC while aiming to operate with no liquid electrolyte. The talk will accordingly review first some interesrting key features of materials and processes in AFCs about which I learnt to large degree from discussions with Russ. The first subject is the ORR in highly alkaline electrolytes and why gold is actually the active catalyst component in AFCs , being used as a AuPt alloy with the Pt serving basically just to harden the metal catalyst particles. The high activity of gold as ORR catalyst in alkaline media, does not fit the extensively taught volcano shaped relationship between M-O bond strength and ORR rate at a metal M. Gold is way down on the descending branch of the volcano plot in acid electrolyte, but is more active than Pt in alkaline media. This has been explained by the stabilization of a peroxide anion intermediate formed in alkaline electrolytes (HO2- or O2=) on the electrolyte side of the metal electrolyte interface, thereby removing the need of significant M-O interaction to stabilize the HOOads intermediate formed in acid. This opens the door for metals of very low affinity to oxygen to become very active ORR electrocatalysts thanks to the low overpotential they require to generate oxide free metal sites. In the case of the AMFCs, this high activty of coinage metals in alkaline environment is the basis for the possible choice of silver as ORR electrocatalyst, having activity in alkaline electrolyte comparable to Pt, at a market price which is about 1% that of Pt. One central aspect which is quite different in AFCs and AMFCs, is water managment. The AMFC presents a significant challenge of combined tendencies for cathode dry-out and anode flooding, originating form the cell water product forming in the anode ( fuel side) and consumed by the electrode process in the cathode ( air side). The resulting demands of water diffusivity through the cell membrane as well as electrode structures required to achieve passive control of the water profile in the AMFCs , staisfying the ORR consumption and ionomer hydration demands, will be described. Finally, the HOR in alkaline media in general and in the AMFC in particular has received significant attention recently. The rate of the HOR at Pt in 0.1-1MKOH solutions is significantly lower vs. the rate in acid. However, AMFC polarization curves to be presented show a performance that is not much below that of PEMFC in operation on hydrogen and air. This is achieved with HOR catalyzed by very low loadings of Pt in the form of alloy ( or other combination ) with an oxophilic metal component which facilitates completion of the HOR process and, securing optimized cell hydration under current.

Multiresolution Soft Sensors: A New Class of Model Structures for Handling Multiresolution Data

The key quality features of industrial processes are typically obtained offline with a considerable delay and by resort to expensive equipment. To avoid this experimental burden, soft sensors have been developed. However, the current methodologies assume that all variables under analysis have the same level of granularity, while in reality they often present a multiresolution structure, with some variables containing instantaneous information on the process and others representing averages over hours, shifts, or production batches. Furthermore, this multiresolution structure is quite often confused with a multirate or multiscale problem, and therefore a clear definition of their main differences is highlighted. On the basis of this distinction, we propose a new class of model structures that explicitly incorporates multiresolution in industrial soft sensors. Several simulated examples demonstrate that the proposed approach leads to better prediction capabilities than its counterparts and is also robust to...

Identification of key features using topological data analysis for accurate prediction of manufacturing system outputs

Topological data analysis (TDA) has emerged as one of the most promising approaches to extract insights from high-dimensional data of varying types such as images, point clouds, and meshes, in an unsupervised manner. To the best of our knowledge, here, we provide the first successful application of TDA in the manufacturing systems domain. We apply a widely used TDA method, known as the Mapper algorithm, on two benchmark data sets for chemical process yield prediction and semiconductor wafer fault detection, respectively. The algorithm yields topological networks that capture the intrinsic clusters and connections among the clusters present in the data sets, which are difficult to detect using traditional methods. We select key process variables or features that impact the system outcomes by analyzing the network shapes. We then use predictive models to evaluate the impact of the selected features. Results show that the models achieve at least the same level of high prediction accuracy as with all the process variables, thereby, providing a way to carry out process monitoring and control in a more cost-effective manner.

An analysis of key characteristics of the Frank-Read source process in FCC metals

A well-known intragranular dislocation source, the Frank-Read (FR) source plays an important role in size-dependent dislocation multiplication in crystalline materials. Despite a number of studies in this topic, a systematic investigation of multiple aspects of the FR source in different materials is lacking. In this paper, we employ large scale quasistatic concurrent atomistic-continuum (CAC) simulations to model an edge dislocation bowing out from an FR source in Cu, Ni, and Al. First, a number of quantities that are important for the FR source process are quantified in the coarse-grained domain. Then, two key characteristics of the FR source, including the critical shear stress and critical dislocation configuration, are investigated. In all crystalline materials, the critical stresses and the aspect ratio of the dislocation half-loop height to the FR source length scale well with respect to the FR source length. In Al, the critical stress calculated by CAC simulations for a given FR source length agrees reasonably well with a continuum model that explicitly includes the dislocation core energy. Nevertheless, the predictions of the isotropic elastic theory do not accurately capture the FR source responses in Cu and Ni, which have a relatively large stacking fault width and elastic anisotropy. Our results highlight the significance of directly simulating the FR source activities using fully 3D models and shed light on developing more accurate continuum models.

Complexity and model comparison in agent based modeling of financial markets

Agent based models of financial markets follow different approaches and might be categorized according to major building blocks used. Such building blocks include agent design, agent evolution and the price finding mechanism. The performance of agent based models in matching key features of real market processes depends on how these building blocks are selected and combined. For model comparison, both measures of model fit and model complexity are required. Some suggestions are made on how to measure complexity of agent based models. An application for the foreign exchange market illustrates the potential of this approach.

Toward the Darwinian transition: Switching between distributed and speciated states in a simple model of early life.

It has been hypothesized that in the era just before the last universal common ancestor emerged, life on earth was fundamentally collective. Ancient life forms shared their genetic material freely through massive horizontal gene transfer (HGT). At a certain point, however, life made a transition to the modern era of individuality and vertical descent. Here we present a minimal model for stochastic processes potentially contributing to this hypothesized "Darwinian transition." The model suggests that HGT-dominated dynamics may have been intermittently interrupted by selection-driven processes during which genotypes became fitter and decreased their inclination toward HGT. Stochastic switching in the population dynamics with three-point (hypernetwork) interactions may have destabilized the HGT-dominated collective state and essentially contributed to the emergence of vertical descent and the first well-defined species in early evolution. A systematic nonlinear analysis of the stochastic model dynamics covering key features of evolutionary processes (such as selection, mutation, drift and HGT) supports this view. Our findings thus suggest a viable direction out of early collective evolution, potentially enabling the start of individuality and vertical Darwinian evolution.

Operational loss modelling for process facilities using multivariate loss functions

This paper presents a methodology to develop multivariate loss functions to measure the operational loss of process facilities. The proposed methodology uses loss functions to provide a model for operational loss due to deviation of key process characteristics from their target values. Having estimated the marginal loss functions for each monitored process variable, copula functions are then used to link the univariate margins and develop the multivariate loss function. The maximum likelihood evaluation method is used to estimate the copula parameters. Akaike's information criterion (AIC) is then applied to rank the copula models and choose the best fitting copula. A simulation study is provided to demonstrate the efficiency of the copula estimation procedure. The flexibility of the proposed approach in using any form of the symmetrical and asymmetrical loss functions and the practical application of the methodology are illustrated using a separation column case study.

Impact of KIBRA Polymorphism on Memory Function and the Hippocampus in Older Adults.

The single nucleotide polymorphism rs17070145 within the KIBRA gene (kidney and brain expressed protein) has been associated with variations in memory functions and related brain areas. However, previous studies yielded conflicting results, which might be due to divergent sample characteristics or task-specific effects. Therefore, we aimed to determine the impact of KIBRA genotype on learning and memory formation, and volume, microstructural integrity and functional connectivity (FC) of the hippocampus and its subfields in a well-characterized cohort of healthy older adults. One-hundred and forty subjects (72 women, age 50-80) were KIBRA genotyped and memory was tested using the Auditory Verbal Learning Task. Also, subjects underwent structural and resting-state functional magnetic resonance imaging at 3T. Subfields were delineated using automated segmentation (FreeSurfer software). Microstructural integrity was measured using mean diffusivity (MD) derived from diffusion tensor images. Seed-based analyses were used to assess FC patterns of the hippocampus. KIBRA T-allele carriers showed a trend for better memory performance, and in the hippocampus significantly higher volumes and partly lower MD, indicative for better microstructure, compared with non-T-allele carriers in the cornu ammonis (CA)2/3 and CA4/dentate gyrus subfields (all P⩽0.008, Bonferroni corrected). Also, T-allele carriers exhibited lower FC of the left hippocampus with areas outside the synchronized HC network. In sum, we could show for the first time that older T-allele carriers exhibited larger volumes and better microstructure within those hippocampus subfields that are implicated in long-term potentiation and neurogenesis, key features of memory processes. Moreover, T-allele carriers showed a more selective FC network of the hippocampus.

Finite element simulation and experimental validation of pulsed laser cutting of nitinol

Nitinol (NiTi) alloys are widely used in laser cutting of cardiovascular stents due to excellent biomechanical properties. However, laser cutting induces thermal damage, such as heat affected zone (HAZ), micro-cracks, and tensile residual stress, which detrimentally affect product performance. The key process features such as temperature distribution, stress development, and HAZ formation are difficult to measure experimentally due to the highly transient nature. In this study, a design-of-experiment (DOE) based 3-dimensional (3D) finite element simulation was developed to shed light on process mechanisms of laser cutting NiTi. The effects of cutting speed, peak pulse power, and pulse width on kerf width, temperature, stress, and HAZ were investigated. A DFLUX user subroutine was developed to model a moving volumetric (3D) heat flux of a pulsed laser. Also, a material user subroutine was used that incorporated superelasticity and shape memory of NiTi.