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The reliable particle parameters of biomass for discrete element method (DEM) pose a challenge in reproducing actual conditions in a simulation environment. However, the calibration of simulation parameters was not seriously considered in industrial or academic fields. In order to adjust the main material parameters and contact parameters of biomass materials in DEM simulation, a calibration procedure based on the comparison between the experimental angle of repose (AoR) by using 3D scan in laboratory and the automated measurement of the numerical AoR (aMAoR) is proposed in this study. A novel data processing algorithm is developed for aMAoR and integrated in a self-developed graphic user interface (GUI). Influences of GUI input variables on AoR acquisition are discussed through several examples. In case studies with four biomass materials, this method prevented the subjective selection of AoR, improved the identification accuracy and therefore decreased minimum amount of suitable parameter combinations for DEM calibration. [Display omitted] • The first study to obtain simulated AoR without influence of subjective selection. • The whole conical surface of heap is taken into consideration for more precise AoR. • Case studies on four representative biomass were used to verify the applicability. • The method is less time-consuming and provides valid result for DEM calibration. [ABSTRACT FROM AUTHOR]

The increasing demand for flexibility in automated production systems also affects the automated material flow systems (aMFSs) within these systems and, thus, demands reconfigurable systems. However, the centralized control concept usually applied in aMFSs hinders easy adaptation, as the entire control software has to be retested when subparts of the control are manually changed. As adaption and subsequent testing are time-consuming tasks, concepts are required for splitting the control from one centralized node to multiple, decentralized control nodes. Therefore, this article presents a holistic, agent-based control concept for aMFSs, whereby the system is divided into so-called automated material flow modules (aMFMs), each controlled by a dedicated module agent. The concept allows reconfiguring an aMFS consisting of heterogeneous, stationary aMFMs, during runtime. Furthermore, it includes aspects such as uniform agent knowledge bases through metamodel-based development, a communication ontology considering different information types and properties, strategic route optimization in decentralized control architectures, and a visualization concept to make decisions of the module agents comprehensible to operators and maintenance staff. We performed the concept evaluation using material flow simulations and a prototypical implementation on a lab-sized demonstrator. Note to Practitioners—Currently, the adaption of automated material flow systems (aMFSs) concerning their layout requires modifications to the control software, including extensive testing. This conflicts the demand for flexible and reconfigurable aMFSs due to changing requirements throughout a system’s life cycle. A promising approach is the modularization of aMFSs, including their control to ease layout changes and adaptations to changing material flows (known as Plug and Produce in the scope of Industrie 4.0). However, common concerns when implementing agent-based control are the real-time capability of the controlled systems and the trust of operators in the automation regarding the safety and security of these systems. More precisely, it is feared that operators might be unable to distinguish the benevolent and malevolent behaviors of an aMFS if agents make decisions autonomously. The concept we present here addresses these issues by establishing different communication types classified according to varying real-time requirements, and by supporting operators via a human–machine interface to make agent decisions comprehensible. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The extensive utilization of fossil fuels is limited by the environmental problems related to carbon dioxide and other greenhouse gas emissions. As a result, the generation of energy and heat from biomass has become increasingly economically and ecologically important in recent decades. In existing numerical studies, the effect of slope form on airflow within the biomass gasifier was not systematically investigated. To explore the potential impact of the real shape of a fuel bed on local gasification conditions, a small-scale updraft fixed-bed gasifier was modeled in this study with a default lateral feeding position and air supply from below. The discrete element method (DEM) was combined with computational fluid dynamics (CFD) by using two open-source software: LIGGGHTS® and OpenFOAM®. The particle models of two biomasses (olive stone and almond shells) were calibrated and used to investigate qualitatively and quantitatively the airflow patterns, including the resulting uneven pressure and temperature distribution at/over the slope surface. Besides, the effects of operating parameters of the feeding system on slope form were highlighted. Therefore, the real fuel heap shape should be taken into consideration for future simulation studies on side-fed, fixed bed gasifiers. [Display omitted] • Material models for DEM simulation are calibrated and validated. • The influences of heap shape and inlet air velocity are explored comprehensively. • Quantitative discussion on the rates of temperature and pressure change. • Suggests that different screw feeding speeds can realize dynamic combustion optimization. • Limitations of a particular DEM-CFD co-simulation model are addressed. [ABSTRACT FROM AUTHOR]

3D concrete printing, as a vital construction method for future development, has lately received significant attention. Unlike classic pumped concrete material extrusion process, a near-nozzle continuous mixer reduces mixing energy by eliminating long conveying distances. However, there has been limited research on it mixing quality. In the study, the first mixing phase of an innovative near-nozzle continuous mixer is explored by full-scale numerical simulation. In order to improve the mixing process of additive 3D concrete printing, it is necessary to compare the mixing efficiencies and performances of continuous mixers with different configurations under various working conditions. Sixty cases with four parameters (sideward angle, the number of paddles, rotation speed, and mixture's composition ratio in different levels) are analyzed in the discrete element method (DEM) simulation. The influences of these parameters on overall and local mixing performances are determined with the modified Lacey mixing index, the spatial Lacey mixing index (SLMI). For unequal particles introduced in this study, SLMl is combined with the equivalent volume method. Qualitative and quantitative analysis results reveal that the four-15°-paddle conveyor mixer performs better above a certain rotation speed, regardless of the mixture's composition ratio. This paper increases the understanding of the mixing process for preparing well-mixed dry materials in a continuous conveying state, which is crucial for efficient near-nozzle mixing in 3D concrete dosing. [Display omitted] • Mixing state with respect to mixer design is explored comprehensively with DEM. • The mixing of non-equal-sized particles are firstly evaluated using SLMI and EVM. • Impact factors on efficiency of near-nozzle mixing are highlighted. • The effect of paddle number becomes more insignificant at higher rotation speeds. • Suggested sideward angle of paddle can optimize mixing performance significantly. [ABSTRACT FROM AUTHOR]

Differential‐algebraic equations (DAEs) can model constrained dynamical systems and processes from practical engineering. Therefore, research on nonlinear optimal control problems of DAEs is of theoretical significance for optimal control of constrained systems, which can generate reference trajectories and control inputs for online control strategies. In terms of the numerical solution of this type of problem, research on indirect numerical methods is still insufficient and less research focuses on symplectic‐preserving methods. In this article, a symplectic indirect approach is proposed for optimal control problems subject to index‐1 DAEs. Necessary conditions of the optimal control problem constitute a Hamiltonian boundary value problem (HBVP) and there exists a symplectic structure in the Hamiltonian system. In the proposed approach, based on specified properties of generating functions, discrete equations can preserve the symplectic structure of the Hamiltonian system. In the iterative solution, the Jacobian matrices of the discrete equations are sparse and symmetric, which are very significant to save memory and improve efficiency in practical computation. In numerical examples, the proposed approach can provide highly accurate state variables and control inputs with fewer iterations. More accurate cost functional can be obtained. Problems from the chemistry process also can be solved effectively, it verifies the problem‐solving ability of the proposed approach. [ABSTRACT FROM AUTHOR]

According to angle of repose theory, non-cohesive bulk solid heaps will form a perfect conical heap if carefully poured. However, there are indications that global shape deviations from an ideal cone exist in experimentally generated heaps. For example, rounded heap tips and concave or convex heap shapes have been reported in the literature, but no studies exist concerning local shape deviations from spatial data. The main aim of this study was to compare the 3D-scanned spatial data from experimentally generated heaps of eight bulk materials with their respective counterparts according to angle of repose/continuum theory and to examine the differences both qualitatively and quantitatively. The results showed that mapping the differences between 3D scans and ideal cones onto a two-dimensional developed view of the ideal cone's lateral surface is a meaningful method for visualizing the heaps' real shapes and for illustrating global as well as local shape deviations. Furthermore, the averaged shapes and respective variances on the experimental heaps' circumferences were examined. Global shape deviations could be identified for lignite, milkpowder, limestone and coke. An important conclusion was that simple consideration of the averaged surface lines does not enable identification of rotationally asymmetric heaps; however, it can be useful for indicating global shape deviations. Large surface line variance implies considerable rotational asymmetry, however the converse conclusion is false. [ABSTRACT FROM AUTHOR]