Process Modeling Approach Research Articles

A new modeling approach for denitrification taking internal chemical gradients into account

AbstractIn this paper a new modeling approach for denitrification and similar processes, which depend on the geochemical gradient between the air-filled larger pores in a soil and a water-filled matrix, is presented. The new modeling approach is capable of taking soil structural properties (obtained e.g. from X-ray CT) into account without requiring a high-resolution simulation. The model approach is explained and its application is demonstrated by simulating denitrification experiments conducted with repacked soil samples to assess the challenges and possibilities of the new approach. The main result of the modeling is that the nitrous oxide emission measured in the experiment can not be explained by a limited supply with oxygen alone at a carbon turnover rate derived from carbon dioxide emissions. It is additionally necessary that the microbial activity is concentrated in localized hot spots to create anaerobic conditions. This is confirmed by analytical solutions.

Optimal Control of Service Systems with Heterogeneous Servers and Priority Customers

We study service systems with parallel servers and random customer arrivals and focus on the waiting cost of customers. Using a Markov decision process (MDP) modeling approach, we analytically characterize the structures of the optimal dynamic server assignment policies for two important systems, one consisting of multiple homogeneous servers and two classes of customers and the other consisting of two heterogeneous servers and multiple classes of customers. Based on the obtained results, we propose a threshold-type heuristic policy for the generalized system consisting of multiple heterogeneous servers and multiple classes of customers. To design such a heuristic policy, we first develop techniques for the performance evaluation of general threshold-type policies with any given threshold values. We then construct a path to search for the optimal threshold values. We compare the performance of the best threshold-type heuristic policy with that of the optimal policy and show that our proposed heuristic policy is computationally efficient yet generates great performance. To derive additional managerial insights, we compare the system under our threshold-type dynamic server assignment policy with other commonly seen and simple systems, such as the dedicated system and the work-conserving flexible priority system. The clear performance advantage observed from extensive numerical experiments demonstrates the importance and usefulness of dynamic server assignment control for systems serving multiple classes of customer arrivals. Finally, we extend our analysis to incorporate customer-dependent service rates and sojourn-time minimization performance metrics. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported by the Hong Kong Research Grants Council [16500921, 16505819] and the National Natural Science Foundation of China [72201233, 72301222]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01228 .

Capabilities toward adoption of outcome-based contracts

PurposeManufacturing enterprises have started to offer the “outcome” derived from machines with the help of outcome-based contracts (OBCs). Offering OBCs leads to benefits such as increased revenues, stronger customer relationships and sustainability. However, implementing OBCs requires critical capabilities. Existing literature has focused on identifying these necessary capabilities, but the prioritization and interrelationships among them remain unexplored. This study aims to address this gap.Design/methodology/approachOur study employs a hybrid analytical hierarchy process and interpretative structural modeling approach to prioritize and map interrelationships among OBC-related capabilities within small and medium-sized enterprises (SMEs).FindingsThe findings highlight the importance of digitalization capabilities such as data privacy and security, remote monitoring, and data analytics; and organizational and governance capabilities, including quantifying, controlling, and monitoring risks, teamwork, and leadership, are highlighted.Research limitations/implicationsWe quantitatively prioritize OBC capabilities and establish their level-wise structural interrelationships, which will facilitate a more effective and efficient implementation of OBCs. Due to the emergent nature of OBCs, our study could identify just one SME case company meeting our selection criteria.Originality/valueExisting OBC literature focusses on the design of OBCs in large companies. Similarly, earlier capability-related OBC literature is oriented toward identifying the OBC capabilities to perform specific functions. However, in the current study, we propose a systematic decision-making approach that comprehensively prioritizes and identifies the interrelationships among the capabilities necessary to provide OBCs, thus complementing the existing scientific literature on OBCs. In addition, we focus on SMEs, that have specific limitations and characteristics.

Gaussian process regression on multiple drivers and attributes for rapid prediction of maximum flood inundation extent and depth

Traditional high-resolution flood models are too slow for real-time predictions. The most common industry practice is to use a lookup table or interpolation algorithm to derive flood extents from a pre-generated library of flood maps. For the library interpolation approach to be effective, the input flood data need to closely match those in the map library. To effectively emulate complex and dynamic flood behaviour, the interpolation approach should be able to account for multiple flood drivers (such as rivers, tributaries and tides) and attributes (such as shape and timing of the hydrographs). However, a simple extension of existing interpolation algorithms would make them overly complex and need a very large map library to deal with these drivers and attributes. To address this challenge, this study investigates the capability of a Gaussian Process (GP) modelling approach to accommodate the complex influence of multiple flood drivers and attributes, and thus to provide robust, accurate and fast predictions. By training the GP model, it learns the underlying relationships between flood depths and multiple flood drivers and attributes. Model accuracy and speed in predicting maximum flood extents and depths are examined. In a case study of a floodplain in Port Fairy, Australia, the GP model is found to generally outperform the library interpolation approach in accuracy, particularly for complex floods, while being similar efficient. The GP model is a promising approach for real-time flood predictions.

Statistical Mapping of PFOA and PFOS in Groundwater throughout the Contiguous United States.

Per-and polyfluoroalkyl substances (PFAS) are synthetic chemicals that are increasingly being detected in groundwater. The negative health consequences associated with human exposure to PFAS make it essential to quantify the distribution of PFAS in groundwater systems. Mapping PFAS distributions is particularly challenging because a national patchwork of testing and reporting requirements has resulted in sparse and spatially biased data. In this analysis, an inhomogeneous Poisson process (IPP) modeling approach is adopted from ecological statistics to continuously map PFAS distributions in groundwater across the contiguous United States. The model is trained on a unique data set of 8910 PFAS groundwater measurements, using combined concentrations of two PFAS analytes. The IPP model predictions are compared with results from random forest models to highlight the robustness of this statistical modeling approach on sparse data sets. This analysis provides a new approach to not only map PFAS contamination in groundwater but also prioritize future sampling efforts.

Automatic activity-travel sequence generator using language, grammar, and machine theory

ABSTRACT Activity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data.

Lower motivation for rewarded mental effort in tobacco dependence.

Tobacco dependence is characterized by decision-making impairments, which may increase the risk of smoking relapse by lowering the capacity to resist the immediate gratification of nicotine consumption. Because controlling one's desires for immediate rewards is experienced as effortful, aversion to effortful control processes may also influence the prospects of successful smoking cessation. We therefore tested whether persons who smoke, compared with persons who do not smoke, show a lower willingness to engage in goal-directed mental effort. Thirty-seven persons who smoke and 38 persons who do not smoke performed a decision task requiring choices on whether to exert a demanding attention task for monetary rewards. Using state-of-the-art drift-diffusion modeling, we found that persons who smoke showed a stronger starting bias toward effort-free rewards. Taken together, our process model approach allowed us to identify the subcomponents of the decision process underlying the stronger aversion against mental effort in tobacco dependence, which may contribute to altered decision making by lowering the motivation to engage in effortful control processes when trying to suppress the desire for nicotine consumption. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A process model approach to integrate future climate change into corporate strategies

To prepare for climate-related risks, a scientifically sound forward-looking integration and disclosure of climate-related data is key. Furthermore, companies also face an increasing demand in non-financial reporting, such as the use of climate change scenarios as part of risk analyses as well as for meeting the Technical Screening Criteria for a substantial contribution or Do No Significant Harm (DNSH) to the environmental goals to show alignment of economic activities with the EU Taxonomy Regulation. Currently, however, companies are still leaving notable gaps in the incorporation of forward-looking climate data and scenarios into reporting and long-term planning strategies. Against this background, the paper describes and discusses the development and concept of an eight-step process model as an innovative way to integrate future climate change into corporate processes. This problem-driven approach is developed in close collaboration and co-creation with companies. It comprises the following phases: i) inventory, ii) prioritization, iii) identification, iv) availability, v) enablement, vi) derivation and implementation of measures, vii) evaluation, and viii) process reinforcement. Also taking into account existing regulations, standards and recommendations for risk assessment and reporting, the process model aims to enable decision makers in companies to identify and assess climate change-related impacts and to take regional climate change information into account. Based on this information – combined with additional local knowledge –, companies can identify suitable adaptation options and develop transformative risk-management solutions. This includes new forms of collaboration, embedded in a framework of a transdisciplinary co-production process. Therefore, a close and trust-based cooperation between internal and external stakeholders right from the outset is a key factor for success.

Comparing effects of different goal type orders on collaborative simulation‐based inquiry in engineering problem solving: A process modelling approach

Comparing effects of different goal type orders on collaborative simulation‐based inquiry in engineering problem solving: A process modelling approach

Modelling Approaches of the Dispersion Process for Conductive Slurries in Chemical Process Industries

In this article, the review on different modeling approaches used for the dispersion of conductive slurries is presented. It comprises three parts: state‐of‐the‐art dispersion process, physiochemical properties, and different modelling approaches. The first part explains the physical mechanism involved in the mixing process and gives an in‐depth understanding of the applicability of the current techniques available commercially with respect to lab‐scale, pilot plant, and industrially upscaled production of these conductive slurries. The main challenges in slurry formulation prevent the formation of agglomerates and breaking down the preexisting agglomerates. It can be understood by studying the role of process parameters such as mixing time and stirring speed involved in the dispersion process. The second part focusses on the important physiochemical properties such as solid content, particle size distribution, and rheology that influences the electrode performance. The third part focusses on the available modelling approaches based on computational‐fluid‐dynamics‐ and coarse‐grained‐molecular‐dynamics‐based on the need as well as the complexity involved. The important aspects such as accuracy, computational cost, advantages, and limitations for both these approaches are discussed that will help the readers to select an appropriate technique in the modelling paradigm to reduce the energy consumption in the dispersion process.

Development Secured Blockchain Based on Agricultural Food Supply Chain in Management System

Abstract: Blockchain, smart contracts, and the Internet of Things (IoT) are essential technologies for the business process reengineering of supply chains in the era of Industry 4.0. The agricultural food supply chain is one of the research areas where these disruptive technologies can play a crucial role in automating business processes, providing real-time goods monitoring, and securing transactions. With the help of blockchain, smart contracts, and the IoT, a product’s health and environment can be monitored throughout the supply chain. In this study, we have critically examined the relevance of these technologies through various activities of the agriculture supply chain using the approach of Business Process Modeling (BPM). Agriculture is a big part of the economy of any country because it helps feed the entire population. It connects and communicates with all of the related industries. If the agriculture base is strong, it is generally regarded as a socially and politically stable society. Many modern farms make use of cutting-edge technology and sciatic and technological ideas. The maximization of the profits relies on some farmers’ vegetables and fruits with chemicals. Chemical fertilizers, insecticides, and other compounds are used in several plants and fruits.

Implementation of mechanistic modeling and global sensitivity analysis (GSA) for design, optimization, and scale-up of a roller compaction process

The pharmaceutical industry has been shifting towards the application of mechanistic modeling to improve process robustness, enable scale-up, and reduce time to market. Modeling approaches have been well-developed for processes such as roller compaction, a continuous dry granulation process. Several mechanistic models/approaches have been documented with limited application to high drug-loaded formulations. In this study, the Johanson model was employed to optimize roller compaction processing and guide its scale-up for a high drug loaded formulation. The model was calibrated using a pilot-scale Minipactor and was validated for a commercial-scale Macropactor. Global sensitivity analysis (GSA) was implemented to determine the impact of process parameter variations (roll force, gap, speed) on a quality attribute [solid fraction (SF)]. The throughput method, which estimates SF values of ribbons using granule production rate, was also studied. The model predicted SF values for all 14 Macropactor batches within ± 0.04 SF. The throughput method estimated SF with ± 0.06 SF for 7 out of 11 batches. GSA confirmed that roll force had the largest impact on SF. This case study represents a process modeling approach to build quality into the products/processes and expands the use of mechanistic modeling during drug product development.

Techno-Economic Analysis of Hemp Production, Logistics and Processing in the U.S

Industrial hemp is a versatile crop, and its products have important applications in the food, cosmetic, pharmaceutical, textile, paper and composite industries. Since its legalization in the U.S. in 2018, interest in growing and using hemp has been increasing. This study evaluated the techno-economics of hemp grain and fiber production, harvest and post-harvest logistics, the drying and storage of hemp grain, and the decortication of fiber stalks. The analysis was performed using a process modeling approach with data obtained from the literature considering a farm size of 162 ha (average U.S. farm size). The input parameters were used as distributed functions and the results obtained are reported as interquartile ranges after 10,000 Monte Carlo simulations. The total cost of producing and processing hemp grain and fiber was estimated to be in the interquartile range of USD 2911–3566 Mg−1 and USD 1155–1505 Mg−1, respectively. The costs of seed and fertilizer along with grain and fiber yields were found to be the major factors influencing field production costs, while costs associated with facilities and labor were the main costs in fiber processing. Despite the high resource requirements and processing costs, high-value applications of hemp grain and fiber show great potential to produce net incomes of USD 426–3701 Mg−1 and USD 1570–2016 Mg−1, respectively.

Developing the Contribution of Information Processing Activities in Forming Persuasion

Research on persuasion or attitude change has been approached through singleprocess models. The focus of this research is to explain more clearly that persuasion only occurs through a process that is directly caused by social influence. However, the use of this approach produced contradictory results. On the one hand, social influence increases persuasion, but other research finds the opposite. This article discusses the possibility of using a dual-process model approach, which is expected to address the inconsistency of social influence on persuasion in single-process models. Dual process models are proposed to be able to provide new explanations regarding the possibility of multiple effects of social influence variables on persuasion by including information processing activity variables. Through the dual process model approach, it is possible to explain that a certain effect can be caused by different processes and that a certain variable can work differently and in different situations. Social influence has different mechanisms for determining persuasion, which can act as a simple cue when the thinking level is low and can have more than one role (multiple roles) when information processing activities are at a high thinking level.
 Keywords: elaboration likelihood models, information processing, persuasion, social influence

Process modeling and simulation of Gmelina arborea (GmW) and Mansonia altissima (MaW) wood drying

This study simulate the process modeling of drying characteristics of Gmelina arborea (GmW) and Mansonia altissima (MaW) wood under the influence of various process variables such as drying time, drying temperature, and airflow velocity. GmW and MaW moisture desorption isotherms, kinetics, and thermodynamics were also studied. Five (5) thin-layer and desorption isotherm drying models were used to model the moisture ratio and water activity data from the process. According to the anatomical analysis, the GmW sample has an average lumen size of 147.44 m, indicating a high moisture content. The results showed that the Guggenheim, Anderson, and de Boer (GAB) model with the lowest sum of squared error value (0.046) demonstrated the best-fit to the experimental desorption data for GmW samples and the Henderson-P model for MaW samples, while the Demir et al. model emerged as the best kinetics model fit for describing the moisture desorption isotherm and thin-layer drying kinetics. GmW effective diffusivity (Deff) values ranged from 3.671 × 10−8 to 5.378 × 10−8 m2/s and MaW effective diffusivity (Deff) values ranged from 2.923 × 10−8 to 4.678 × 10−8 m2/s. GmW and MaW activation energies were 252.702 kJ/mol and 313.604 kJ/mol, respectively. The thermodynamic studies revealed that the heat and mass transfer coefficients varied linearly with temperature, as the change in enthalpy (ΔH) and change in entropy (ΔS) decreased while the Gibbs free energy (ΔG) increased. The results obtained from this study demonstrated that the proposed drying process modeling and simulation approach could be successfully applied to investigate the wood drying phenomena. The information can be used to reduce the drying costs and improve the wood quality.

Do emotional regulation behaviors matter in EFL teachers' professional development? A process model approach

The effectiveness of pedagogical approaches to teaching English as a foreign language (EFL) can be enhanced by ensuring that teachers are in a psychologically sound state, allowing them to upgrade their professional skills. Emotional regulation (ER) is acknowledged as a key factor in enhancing one’s professional capabilities. However, research in this area is notably underdeveloped. Therefore, this study aims to investigate the utilization level and impact of ER strategies on EFL teachers’ professional development in Chinese higher educational institutions. The research used a survey approach, employing questionnaires as the primary means of data collection. The data was then analysed through structural equation modelling and a descriptive statistical approach. The findings provide evidence that EFL teachers effectively employ various ER strategies to navigate the challenges inherent in the classroom, underscoring their adaptability and resilience. Moreover, the study identified a significant relationship between the use of ER and EFL teachers’ professional development. These findings highlight the essential role of emotional regulation in the teaching journeys of EFL educators and underscore the importance of integrating emotional regulation training into teacher development programs. FUNDING INFORMATION. This study is supported by the Education and Teaching Research Project of Henan Province, China (Grant No. 2022SYJXLX051), and the Education and Teaching Research Project of the International Cooperation and Exchange Division, Ministry of Education, China. (Grant No. (2023) No. 156).

Digital rock advances from a material point method approach for simulation of frame moduli and a sedimentary petrology-inspired method for creation of synthetic samples through simulation of deposition and diagenesis

We compare hydromechanical simulation results that use two alternative sources of 3D digital rock input: micro-CT analysis and “synthetic rocks” created by using a newly developed process simulation methodology that more rigorously reflects knowledge from sedimentary petrology compared with previous efforts. We evaluate the performance of these alternative representations using St. Peter Sandstone samples where “dry” static bulk modulus ( K) and shear modulus ( G) are simulated using a new extension of the material point method that resolves contacts using high-resolution surface meshes and considers three alternative contact modeling approaches: “purely frictional,” “fully bonded,” and “cohesive zones.” We evaluate the model performance on two samples from the data set with multiple static moduli measurements (sample 1_2: porosity 24.6 vol%, K 10.2–14.7 GPa, and G 11.6–14.0 GPa; sample 11_2: porosity 12.4 vol%, K 13.5–24.6 GPa, and G 12.8–17.9 GPa). Purely frictional results underpredict measured modulus values, whereas fully bonded results overpredict them. Measured values are most closely approximated by results with cohesive zones that consider sets of discrete spring-like features at contacts. In contrast, shear modulus results from finite-element model simulations on structured grids tend to be significantly greater than measured values, particularly for samples with <18 vol% porosity. Permeability values from digital rock-physics simulations for the studied samples are within factors of 2–5 of conventional core analysis measurements (2860 and 58 mD for samples 1_2 and 11_2, respectively). We determine that the process modeling approach (1) accurately reproduces the measured rock microstructure parameters from thin-section analysis, (2) leads to simulation results for dry static moduli and permeability with accuracy comparable to simulations that use micro-CT samples, and (3) provides a rigorous basis for predicting diagenetically induced variations in hydromechanical properties over the range from unconsolidated sand to indurated rock.

Process models of verbal memory in cancer survivors: Bayesian process modeling approach to variation in test scores

ABSTRACT Introduction Verbal memory is a complex and fundamental aspect of human cognition. However, traditional sum-score analyses of verbal learning tests oversimplify underlying verbal memory processes. We propose using process models to subdivide memory into multiple processes, which helps in localizing the most affected processes in impaired verbal memory. Additionally, the model can be used to address score and process variability. This study aims to investigate the effects of cancer and its treatment on verbal memory, as well as provide a demonstration of how process models can be used to investigate the uncertainty in neuropsychological test scores. Method We present an investigation of memory process scores in non-CNS cancer survivors (n = 184) and no-cancer controls (n = 204). The participants completed the Amsterdam Cognition Scan (ACS), in which classical neuropsychological tests are digitally recreated for online at-home administration. We analyzed data from the ACS equivalent of a Verbal Learning Test using both traditional sum scores and a Bayesian process model. Results Analysis of the sum score indicated that patients scored lower than controls on immediate recall but found no difference for delayed recall. The process model analysis indicated a small difference between patients and controls in immediate retrieval from both the partially learned and learned states, with no differences in learning or delayed retrieval processes. Individual-level analysis shows considerable uncertainty for sum scores. Sum scores were more certain than single trials. Retrieval parameters also showed less uncertainty than learning parameters. Conclusion The Bayesian process model increased the informativity of Verbal Learning test data, by showing uncertainty of the traditional sum score measurements as well as how the underlying processes differed between populations. Additionally, the model grants insight into underlying memory processes for individuals and how these processes vary within and between them.

A Value Modeling Approach for Product Development Processes and Supply Chain Management

This research presents a quantifiable value modeling approach for tracking and optimizing value addition at different stages of the product development process (PDP) and supply chain management (SCM). While there is a vast abundance of papers in the literature harping on the need to maximize the value, especially in lean thinking context, in PDP and SCM, there is a dearth of propositions on how to quantitatively measure and optimize value at each stage of product development and supply chain. This paper presents a multi-dimensional value modeling approach that incorporates parameters and variables from different stages of PDP and SCM by mathematically mapping them on to two macroscopic metrics, namely Product Information Evolution and Risk Reduction. These two metrics are in turn modeled by incorporating value attribute variables such as performance, risk, schedule, cost, form, fit, function and timeliness from different stages of PDP-SCM such as customer needs ideation, product functions, inventory management, logistics management and product delivery. The efficacy of the proposed model was tested by utilizing it to measure value additions at different stages of PDP-SCM as well as the total value addition for an ordinance disposal (OD) robot product family, once developed and supplied using traditional production method and once using lean production method, with the underlying axiom being the lean method will result in higher value. To ensure a fair and accurate comparison, both production methods used the proposed mathematical value model and worked toward maximizing total value using nonlinear programming. The results showed that lean PDP-SCM method added higher value at each of the stages as well as total value when compared to the traditional PDP-SCM method thus establishing the proposed value model as a viable mathematical way to measure value. While the proposed value model passed test of efficacy against the lean axiom, further research is needed to test it against other established axioms to establish it as a robust mathematical way to measure value in PDP-SCM.

Modeling of bioprocess pre-stages for optimization of perfusion profiles and increased process understanding.

Improving bioprocess efficiency is important to reduce the current costs of biologics on the market, bring them faster to the market, and to improve the environmental footprint. The process intensification efforts were historically focused on the main stage, while intensification of pre-stages has started to gain attention only in the past decade. Performing bioprocess pre-stages in the perfusion mode is one of the most efficient options to achieve higher viable cell densities over traditional batch methods. While the perfusion-mode operation allows to reach higher viable cell densities, it also consumes large amount of medium, making it cost-intensive. The change of perfusion rate during a process (perfusion profile) determines how much medium is consumed, thereby running a process in optimal conditions is key to reduce medium consumption. However, the selection of the perfusion profile is often made empirically, without full understanding of bioprocess dynamics. This fact is hindering potential process improvements and means for cost reduction. In this study, we propose a process modeling approach to identify the optimal perfusion profile during bioprocess pre-stages. The developed process model was used internally during process development. We could reduce perfused medium volume by 25%-45% (project-dependent), while keeping the difference in the final cell within 5%-10% compared to the original settings. Additionally, the model helps to reduce the experimental workload by 30%-70% and to predict an optimal perfusion profile when process conditions need to be changed (e.g., higher seeding density, change of operating mode from batch to perfusion, etc.). This study demonstrates the potential of process modeling as a powerful tool for optimizing bioprocess pre-stages and thereby guiding process development, improving overall bioprocess efficiency, and reducing operational costs, while strongly reducing the need for wet-lab experiments.