Results 11 to 20 of about 126,259 (267)

The combined effect of inorganic salt and ionic liquid in pretreatment on enzymatic saccharification of rice straw [PDF]

E3S Web of Conferences, 2022
The pretreatment method is one of the challenging steps in the production of biofuel through the biorefinery process that unlocks the recalcitrant nature of lignocellulosic biomass. Ionic liquid pretreatment gained attention for being highly effective to
Jose Diana, Raina Neelu, Deepakkumar Rajagopal, Panakkal Elizabeth Jayex, Sriariyanun Malinee, Kangsadan Tawiwan   +5 more
doaj   +1 more source

Automated application processing

Sādhanā, 2022
Recruitment in large organisations often involves interviewing a large number of candidates. The process is resource intensive and complex. Therefore, it is important to carry it out efficiently and effectively. Planning the selection process consists of several problems, each of which maps to one or the other well-known computing problem.
Eshita Sharma, Keshav Gupta, Lubaina Machinewala, Samaksh Dhingra, Shrey Tripathi, V S Shreyas, Sujit Kumar Chakrabarti   +6 more
openaire   +3 more sources

Mathematical models and methods of prolongation of operating lifetimes for expensive objects [PDF]

MATEC Web of Conferences, 2019
The article is devoted to the relevant issue of prolongation of operating lifetimes for expensive objects. The methods of prolongation of operating lifetimes for complex technical systems have been described.
Trukhanov Vladimir, Kukhtik Mikhail, Fyodorova Natal’ya   +2 more
doaj   +1 more source

Feedback Control of Crystal Size Distribution for Cooling Batch Crystallization Using Deep Learning-Based Image Analysis

Crystals, 2022
The shape of the crystal size distribution directly determines the quality of crystal products. It is often assumed that distributional properties of crystal size conform to the Gaussian distribution or the log normal distribution.
Chenyang Gan, Liangyong Wang, Shunkai Xiao, Yaolong Zhu   +3 more
doaj   +1 more source

Robotic process automation [PDF]

Electronic Markets, 2019
Within digital transformation, which is continuously progressing, robotic process automation (RPA) is drawing much corporate attention. While RPA is a popular topic in the corporate world, the academic research lacks a theoretical and synoptic analysis of RPA.
Peter Hofmann 0001, Caroline Samp, Nils Urbach   +2 more
openaire   +2 more sources

Asynchronous broadcast‐based event‐triggered control for discrete‐time clock synchronization

IET Control Theory & Applications, 2023
Precise timing plays a key role in the time‐sensitive industrial Internet of Things (IIoT). However, precise time synchronization requires more frequent packet exchange, which consumes more communication bandwidth and energy.
Zhian Jia, Xuewu Dai, Dongliang Cui, Yuxiang Hu   +3 more
doaj   +1 more source

Denitrification Performance in Packed-Bed Reactors Using Novel Carbon-Sulfur-Based Composite Filters for Treatment of Synthetic Wastewater and Anaerobic Ammonia Oxidation Effluent

Frontiers in Microbiology, 2022
To avoid nitrate pollution in water bodies, two low-cost and abundant natural organic carbon sources were added to make up the solid-phase denitrification filters.
Yao Wang, Baorui Liang, Fei Kang, Youzhao Wang, Zhihong Yuan, Zhenning Lyu, Tong Zhu, Zhijun Zhang   +7 more
doaj   +1 more source

Multi-Output Soft Sensor with a Multivariate Filter That Predicts Errors Applied to an Industrial Reactive Distillation Process

Mathematics, 2021
The paper deals with the problem of developing a multi-output soft sensor for the industrial reactive distillation process of methyl tert-butyl ether production.
Vladimir Klimchenko, Andrei Torgashov, Yuri A. W. Shardt, Fan Yang   +3 more
doaj   +1 more source

Probabilistic Multi-Step Identification With Implicit State Estimation for Stochastic MPC

IEEE Access, 2023
Stochastic Model Predictive Control (SMPC) is a promising solution for controlling multivariable systems in the presence of uncertainty. However, a core challenge lies in obtaining a probabilistic system model.
Felix Fiedler, Sergio Lucia
doaj   +1 more source

Improved Uncertainty Quantification for Neural Networks With Bayesian Last Layer

IEEE Access, 2023
Uncertainty quantification is an important task in machine learning - a task in which standard neural networks (NNs) have traditionally not excelled. This can be a limitation for safety-critical applications, where uncertainty-aware methods like Gaussian
Felix Fiedler, Sergio Lucia
doaj   +1 more source