Modular Process Research Articles

SMEARcore – modular data infrastructure for atmospheric measurement stations

Abstract. We present the SMEARcore data infrastructure framework: a collection of modular programs and processing workflows intended for measurement stations and campaigns as a real-time data analysis and management platform. SMEARcore enables new SMEAR (Station for Measuring Ecosystem–Atmosphere Relations) stations to be integrated in a way that is consistent with existing stations and transfers the existing data curation experience to the new station. It establishes robust data pipelines that allow easier diagnosis of problems. We show practical examples of how SMEARcore is utilized at operational measurement stations. This work differs from earlier similar concepts, such as those used at stations within ACTRIS (Aerosols, Clouds and Trace Gases Research Infrastructure) and ICOS (Integrated Carbon Observation System) networks, in three important aspects: firstly, by keeping all the processing under the control of the data owners; secondly, by providing tools for making data interoperable in general instead of harmonizing a particular set of instruments; and thirdly, by being extensible to new instruments. As such it is not meant as a replacement for these infrastructures but to be used in addition to them and to bring structured data curation to more measurement stations not yet using these practices.

Preparation of High Geometric Filling Factor Perovskite Module and Feasibility Study on Electrocatalytic Hydrogen Production

In recent years, perovskite solar cells have achieved high efficiency in small areas, but the industrialization of perovskite solar cells is still hampered by the efficiency loss of cells during area scaling. Series modules are currently the most widely used and effective modular process for perovskite solar cells, a process that requires the patterning of the layer stack in individual cells. This interconnection scheme consists of three lines, P1–P3, which are ablated using a pulsed laser beam. In this work, we developed an efficient perovskite module with a high geometric fill factor (GFF) based on a two-step spin-coating method by optimizing the laser ablation step. We constructed the entire device structure by means of a picosecond green pulsed laser, meanwhile, the dead area is greatly reduced by optimizing the parameters and process, and finally achieving an ultra-high GFF of over 99%. In addition, we investigated the effect of poor P2 and P3 ablation on the perovskite solar modules and compared the performance of the module before and after ablation optimization. We finally obtained a maximum photoelectric conversion efficiency of 22.79% with an aperture area of 12.6 cm2. Concurrently, we conducted electrocatalytic hydrogen production experiments on the prepared perovskite solar modules, and the experimental results have also shown excellent performance and good prospects.

Electrospinning-based nanofiber architectures for outstanding CO2 capture

The worsening climate change has made the development of alternative and sustainable technologies to achieve carbon neutrality a pressing issue. Numerous strategies for carbon dioxide (CO2) capture/conversion and relevant material preparation have been dealt with on a case-by-case basis. Therefore, a reconfigurable process to secure optimal procedures and materials is urgently required to reduce CO2 emissions in a convenient, rapidly implementable manner. On this account, this study attempted to construct polyethylenimine (PEI)/cerium oxide (CeO2)/activated carbon nanofibers (ACNFs) as a reliable CO2 adsorbent from an electrospinning (ES)-based architecting as a modular process to accelerate their field applications because adsorption has outstanding advantages for CO2 capture, such as process simplicity, wide operational range, low cost, and stable performance. By selecting ES with commercially available materials (PEI (providing CO2 chemisorption), cerium nitrate hexahydrate (CO2 selectivity), and polyacrylonitrile (base of porous nanofibers)), a composite nanofibrous architecture (PEI/CeO2/ACNFs) with outstanding CO2 capture can be achieved by simple process manipulation (manipulating the electrical polarity applied to the nozzle and concentration of the cerium nitrate for the ES). This study may provide a reconfigurable manufacture to ensure a reliable adsorbent for CO2 capture and base materials for CO2 conversion from electro- and photocatalysis.

Cryptographic protocol for privacy-preserving integration of HAZOPs in modular process plants

Information which is contained in Hazard & Operability (HAZOP) studies is highly sensitive since it can reveal the vulnerabilities of a system and potential ways in which to bypass safeguards. Through the design of systems involving collaboration along a value chain, at some point this information is shared between several parties. In this paper, we propose a methodology for the secure exchange of safety information whilst preserving sensitive information for the application of modular Hazard & Operability (HAZOP) studies. We use homomorphic encryption in a workflow for the sharing of information between plant owners and operators as well as module vendors. We apply encryption to the risks from different modular HAZOPs (mHAZOPs), and combine and compare them without disclosing the risk level. Our contribution is a privacy-preserving protocol for mHAZOP comparison during the integration of modular process and equipment. We provide an exemplary implementation of the protocol and demonstrate the protocol’s privacy and correctness.

Design of Module Type Package Services for Modular Downstream Units and Process Analytic Technology

AbstractModularization of process plants with its standardization activities is one of the current responses to react to dynamic markets, shorter product life cycles, and uncertain supply chains. Standardized solutions for intelligent process equipment assemblies with own automation promise high potential for chemical and pharmaceutical industries. Despite the standardized description of the module type package (MTP) and the corresponding service concept, the implementation of the service logic is left to the manufacturer, which often leads to finding various granular services for different process functions or assemblies. In this contribution, different service design approaches for a generic ‘separate’ service are investigated on the example of a solvent extraction and a distillation column. Additionally, a Raman spectroscope device for process analysis is implemented via MTP with an ‘analyze’ service. Pros and cons of the different service design approaches are discussed in the context of a fast and flexible process development in the laboratory.

A modular approach for multilingual timex detection and normalization using deep learning and grammar-based methods

Detecting and normalizing temporal expressions is an essential step for many NLP tasks. While a variety of methods have been proposed for detection, best normalization approaches rely on hand-crafted rules. Furthermore, most of them have been designed only for English. In this paper we present a modular multilingual temporal processing system combining a fine-tuned Masked Language Model for detection, and a grammar-based normalizer. We experiment in Spanish and English and compare with HeidelTime, the state-of-the-art in multilingual temporal processing. We obtain best results in gold timex normalization, timex detection and type recognition, and competitive performance in the combined TempEval-3 relaxed value metric. A detailed error analysis shows that detecting only those timexes for which it is feasible to provide a normalization is highly beneficial in this last metric. This raises the question of which is the best strategy for timex processing, namely, leaving undetected those timexes for which is not easy to provide normalization rules or aiming for high coverage.

QSP Designer: Quantitative systems pharmacology modeling with modular biological process map notation and multiple language code generation.

Typical Quantitative Systems Pharmacology (QSP) workflows involve discussion of biology, supported by graphical diagrams, followed by construction of large Ordinary Differential Equation models. QSP Designer facilitates this process by providing enhanced graphical notation, which enables hierarchical presentation with modules and handling of combinatorial complexity with diagram node arrays. Whereas the software includes a simulation engine, a major feature is full model code generation in MATLAB, R, C, and Julia to support multiple modeling communities.

Parallel column model for reactive dividing wall column simulation

Reactive Dividing Wall Column (RDWC) modelling has attracted significant attention in recent years because simulations of processes containing such columns have demonstrated the potential for significant savings in energy and capital cost. Most modelling of these columns involves decomposing RDWCs into a thermodynamically equivalent flowsheet of reactive and non-reactive column sections in a sequential modular process simulation package. Such simulations of (R)DWCs can be time consuming to create and difficult to converge. It has been suggested that equation-oriented solvers can have superior numerical performance. Here, we use a CAPE-OPEN compliant equation-based parallel column model (PCM) that enables the rapid modelling of an RDWC as a single self-contained unit. Some conventionally modelled RDWCs from the literature are reproduced with the PCM to demonstrate its validity and excellent numerical performance. The ease and rapidity with which different RDWC configurations can be created is a significant advantage of our PCM.

From Activity Recognition to Simulation: The Impact of Granularity on Production Models in Heavy Civil Engineering

As in manufacturing with its Industry 4.0 transformation, the enormous potential of artificial intelligence (AI) is also being recognized in the construction industry. Specifically, the equipment-intensive construction industry can benefit from using AI. AI applications can leverage the data recorded by the numerous sensors on machines and mirror them in a digital twin. Analyzing the digital twin can help optimize processes on the construction site and increase productivity. We present a case from special foundation engineering: the machine production of bored piles. We introduce a hierarchical classification for activity recognition and apply a hybrid deep learning model based on convolutional and recurrent neural networks. Then, based on the results from the activity detection, we use discrete-event simulation to predict construction progress. We highlight the difficulty of defining the appropriate modeling granularity. While activity detection requires equipment movement, simulation requires knowledge of the production flow. Therefore, we present a flow-based production model that can be captured in a modularized process catalog. Overall, this paper aims to illustrate modeling using digital-twin technologies to increase construction process improvement in practice.

The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0

Abstract. Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least 2 consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and SOL module named the Permafrost and Organic LayEr module for Forest Models (POLE-FM) that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61 000 ha) and over temporal scales of days to centuries. The coupled model generated intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) generally consistent with independent observations in 17 instrumented forest stands. The model also represented the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 39.3 % of forested area in the landscape as underlain by permafrost, compared to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision-making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model's utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.

Process Simulation and Performance Models for Enhanced Modular Refinery Operations in Nigeria

The study aimed at availability of petroleum products in Nigeria through the operation of enhanced modular refinery process due to inefficient conventional major refineries. The enhanced modular refinery converts or processes residue product from conventional modular refinery as feedstock to the hydrocracker reactor for viable and desired products such as liquefied petroleum gas, naphtha and diesel. Therefore, twenty Nigerian crude oil types were classified as sweet, light and medium crude oil, and these crude oil types were categorized based on their recovery volume at true boiling point of 370oC as Group A, Group B and Group C respectively. Thus, based on product output and equipment cost, a modular refinery with 29 trays was used in this study, as light and medium sweet crude oil types were simulated in a modular refinery of various column trays prior to the desired tray. A topping plant with a 30,000 barrel per day capacity and a modified topping plant with 29 trays respectively were used to process different types of Nigerian crude oil. using Aspen Hysys to evaluate their products yield and tray compositions. The modified modular refinery with hydrocracker yielded more valuable from the residue of conventional modular refinery with minimal bottom fraction. Performance models for hydrocracker reactor was developed based on the nature of reaction, kinetic parameters estimated and results compared with experimental data with minimum deviations. The developed performance models predicted feedstock conversion and product yield along the hydrocracker reactor’s dimensionless length by solving a set of ordinary differential equation models. Thus, the hydrocracking process was simulated to evaluate the effects of catalyst effectiveness factor on feedstock conversion and products yield.

Revolutionizing Avocado Oil Production

The Polat modular avocado oil processing unit delivers high product yield and quality.

Robustness Evaluation Process for Scheduling under Uncertainties

Scheduling production is an important decision issue in the manufacturing domain. With the advent of the era of Industry 4.0, the basic generation of schedules becomes no longer sufficient to face the new constraints of flexibility and agility that characterize the new architecture of production systems. In this context, schedules must take into account an increasingly disrupted environment while maintaining a good performance level. This paper contributes to the identified field of smart manufacturing scheduling by proposing a complete process for assessing the robustness of schedule solutions: i.e., its ability to resist to uncertainties. This process focuses on helping the decision maker in choosing the best scheduling strategy to be implemented. It aims at considering the impact of uncertainties on the robustness performance of predictive schedules. Moreover, it is assumed that data upcoming from connected workshops are available, such that uncertainties can be identified and modelled by stochastic variables This process is supported by stochastic timed automata for modelling these uncertainties. The proposed approach is thus based on Stochastic Discrete Event Systems models and model checking techniques defining a highly reusable and modular process. The solution process is illustrated on an academic example and its performance (generecity and scalability) are deeply evaluated using statistical analysis. The proposed application of the evaluation process is based on the technological opportunities offered by the Industry 4.0.

Erweitertes Vorgehensmodell für intentionsbasiertes Engineering und Automatisierung modularer verfahrenstechnischer Anlagen

Abstract The modularization of process plants and their modular automation represent innovative concepts to meet the increasing demands for flexibility in plant operation. However, existing approaches to support the engineering of such plants do not focus on the early phases of requirements elicitation and concept design, although these early phases are crucial for the economic efficiency of the later plant. This paper presents a process model for the formalized description of intentions in the early engineering phases of modular process plants. The plant operator creates a formalized model of his intentions and submits it to the module manufacturers in a suitably modularized form. Thus, in the engineering phases, a reduction of effort and an increase in support in the planning and development of the plants are achieved. Furthermore, purpose-specific, required automation functionalities can be defined at an early stage.

ABC-transporter CFTR folds with high fidelity through a modular, stepwise pathway

The question how proteins fold is especially pointed for large multi-domain, multi-spanning membrane proteins with complex topologies. We have uncovered the sequence of events that encompass proper folding of the ABC transporter CFTR in live cells by combining kinetic radiolabeling with protease-susceptibility assays. We found that CFTR folds in two clearly distinct stages. The first, co-translational, stage involves folding of the 2 transmembrane domains TMD1 and TMD2, plus one nucleotide-binding domain, NBD1. The second stage is a simultaneous, post-translational increase in protease resistance for both TMDs and NBD2, caused by assembly of these domains onto NBD1. Our assays probe every 2–3 residues (on average) in CFTR. This in-depth analysis at amino-acid level allows detailed analysis of domain folding and importantly also the next level: assembly of the domains into native, folded CFTR. Defects and changes brought about by medicines, chaperones, or mutations also are amenable to analysis. We here show that the well-known disease-causing mutation F508del, which established cystic fibrosis as protein-folding disease, caused co-translational misfolding of NBD1 but not TMD1 nor TMD2 in stage 1, leading to absence of stage-2 folding. Corrector drugs rescued stage 2 without rescuing NBD1. Likewise, the DxD motif in NBD1 that was identified to be required for export of CFTR from the ER we found to be required already upstream of export as CFTR mutated in this motif phenocopies F508del CFTR. The highly modular and stepwise folding process of such a large, complex protein explains the relatively high fidelity and correctability of its folding.

Parametric Scaffold Design for Property Control of Multi-Material Structure in Metallic Hybrid Additive Manufacturing

Additive manufacturing (AM) technologies have lots of advantages to fabricate complex structures in both shape and material composition. However, there is rarely a single-step AM process for high-performance ceramic-reinforced metal matrix composite (MMC) with complex geometric shapes. To achieve the fabrication of MMC structures with controlled mechanical properties, this paper proposes a novel design and manufacturing method for the modular hybrid AM process combining a binder jetting sintering process with conventional casting. At first, different material compositions are assigned to these two processes, and then a parametric design method is applied to control the generation of a qualified (self-supported) scaffold, which will be printed as a ceramic reinforcement frame with less AM post-processing. Finally, the whole sintered scaffold is immersed in molten metal for casting control. In this way, by changing the unit cell type, size, and porosity ratio of the parametric scaffold, the composition ratio of materials changes, which results in the change of the structure's mechanical properties. A case study is presented at the end to prove the proposed concept.

Holistic Enlightening of Blackspots With Passive Tailorable Reflecting Surfaces for Efficient Urban mmWave Networks

Mitigating outage regions is of particular interest for emerging millimeter-wave (mmWave) and future sub-terahertz frequency cellular networks. Whereas smart radio environments based on metasurfaces constitute a key concept for 6G research, current networks cannot be served as new control procedures are required. Although small prototypes are already emerging, costs will be higher for the dense deployment of reconfigurable intelligent surfaces (RISs) than for passive reflectors. It is argued that passive reflectors can be used to effectively, cost-efficiently, and permanently boost connectivity in well-defined service areas. Leveraging the advantages of 3D printing and spray-painted conductive varnish, we introduce the Holistic Enlightening of bLackspots with passIve reflectOr moduleS (HELIOS) approach which is characterized by its scalability and parametrizability to meet the reflection requirements derived from sophisticated network planning. These slim reflectors meet the core criteria of ease of installation and minimal visual impact on the cityscape, which are imperative for market success. Our measurements-based comparison of prototypes against typical metal/aluminum reflectors shows at least equal reflectivity at a higher practicality of the proposed manufacturing process. The conducted simulation study validates the modular reflector pattern design process and finds a trade-off between the reflector efficiency and the minimum protrusion depth, which relates to the number of modules in the designated mounting area. An urban ray-tracing simulation-based case study further underlines the high applicability of the proposed approach, with the growth of the beyond line-of-sight (LOS) connectivity region being nearly twice as large for a site-tailored HELIOS configuration as for a simple reflector plate.

Improving detection thresholds and robust event filtering in single-particle and single-cell ICP-MS analysis

In this work, a modular data processing workflow for single-particle (sp) and single-cell (sc) inductively coupled plasma-mass spectrometry (ICP-MS) is presented.

Fault Tolerant Modular Differential Power Processing Converter for Photovoltaic Systems

This paper proposes a fault tolerant modular differential power processing (DPP) converter for photovoltaic (PV) systems under partial shading conditions. The proposed DPP converter can be derived by integrating panel-level and module-level DPP converters, reducing the total switch count, and achieving a simplified circuit. The modular design of the proposed converter allows PV systems to be flexibly scaled up without redesigning converters. Furthermore, series-resonant tanks in the proposed DPP converter exhibit constant-current–constant-voltage characteristics that realize the inherent fault tolerance against short-circuit and open-circuit fault of PV panels. The prototype for a 1.5-kW PV system consisting of two modules, each comprising four panels connected in series, was built and tested. In addition to the fault tolerance, the energy yield enhancement of 19.6% was experimentally demonstrated.

Towards retrieving functionalities from intentions and services in modular automation

The automation of plants in the process industry, especially of modular process plants, is shifting towards a more decentralized and service-based paradigm. Required and offered services need to be described in such a way that they can be either easily described by plant operators and module vendors. This two-sided problem requires a semantic description of needed functionalities from intentions and abstraction of existing services onto offered functionalities. In this contribution, the authors present two algorithms for the modeling and derivation of functionalities from intentions and services in modular automation. The algorithms are based on the idea of functional reasoning which support the explanation and derivation of functions in a system. By using various ontologies, the approach builds upon semantic descriptions and exploits query- and rule-based languages to support the retrieval of functionalities.