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Woody biomass can be converted into a synthesis gas, a mixture of H2, CO, and CO2, by gasification. Removal of refractory tars, produced during biomass gasification, requires highly active catalysts that can be applied in-situ during gasification or in secondary catalytic tar cracking reactors. Subsequently, the water-gas-shift (WGS) reaction adjusts the H2 to CO ratio, prior to the synthesis of the desired products (CH4 as renewable natural gas or RNG, alcohols, hydrocarbons, and olefins). Catalysts play a pivotal role in all processing steps, with recent advances in catalyst development discussed herein with an emphasis on catalyst stability, reaction mechanisms, and structure–activity relationships. The economic viability of biomass conversion to fuels and chemicals is also reviewed and shown to be dependent on end-product, location, and government incentives. [ABSTRACT FROM AUTHOR]

A modified lazy learning algorithm combined with a relevance vector machine (MLL-RVM) is presented to address a data-driven modelling problem for a gasification process inside a united gas improvement (UGI) gasifier. During the UGI gasification process, the measured online temperature of the produced crude gas is a crucial aspect. However, the gasification process complexities, especially severe changes in the temperature versus infrequent manipulation of the gasifier and the unknown noise in collected data, pose difficulties in dynamics process descriptions via conventional first principles. In the MLL-RVM, a novel weighted neighbour selection method is adopted based on the proposed dynamic cost functions. Moreover, the RVM is utilized in the implementation and design of the proposed online local modelling owing to its short test time and sparseness. Furthermore, the leave-one-out cross-validation technique is used for local model validation, by which the modelling performance is further improved. The MLL-RVM is applied to a series of real data collected from a pragmatic UGI gasifier, and its effectiveness is verified.

To address the stable grasping control issue in manipulator grasping systems, this manuscript proposes an improved multiverse optimizer‐based anti‐saturation model‐free adaptive control (IMVO‐AS‐MFAC) algorithm. Initially, the manuscript converts the manipulator grasping system into an equivalent data model through dynamic linearization techniques. Then, based on the dynamic linearization model, the IMVO‐AS‐MFAC controller is designed. To address the actuator saturation problem that commonly occurs during the clamping process of manipulator grasping systems, a saturation parameter is introduced into the IMVO‐AS‐MFAC algorithm. Meanwhile, the controller parameters are optimized using an improved multiverse optimizer algorithm, which involves modifications to the initial population distribution and location update strategy. The improved algorithm demonstrates more competitive optimization performance compared to the traditional multiverse optimizer. The major advantage of the IMVO‐AS‐MFAC algorithm lies in the fact that only the input and output data of the manipulator grasping system are required throughout the entire control process, and the controller parameters are derived using an optimization algorithm rather than relying on empirical knowledge. Furthermore, rigorous mathematical analysis confirms the stability of the IMVO‐AS‐MFAC approach, and its effectiveness is validated through semi‐physical experiments conducted in an environment integrating the MATLAB/Simulink module and the RecurDyn platform. [ABSTRACT FROM AUTHOR]

To solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a novel model‐free adaptive control algorithm with data compensation under constraint conditions (COM‐cMFAC) is proposed in this manuscript. In the COM‐cMFAC algorithm, the pseudo partial derivative (PPD), which is a time‐varying parameter, is used to linearize the nonlinear dynamics of the multivehicle cooperative system by using dynamic linearization technology. Then, a COM‐cMFAC controller is designed. For the case of data packet loss, the proposed controller uses a data compensation mechanism that estimates and compensates for the lost data through the data collected at the previous moment to perform packet loss control. Additionally, the controller considers the constrained input and output problems that will occur in the actual control process and imposes input and output constraints. The main advantage of the COM‐cMFAC algorithm is that the entire control process only needs the input and output data of the multivehicle cooperative system, and it also has a good control effect in the case of packet loss. The stability of the proposed method is verified through strict mathematical analysis, and its effectiveness is verified by semiphysical experiments based on a MATLAB/Simulink and Carsim platform connection environment. [ABSTRACT FROM AUTHOR]

The effects of different sizes of gel pores on the elastic mechanical parameters of calcium silicate hydrate (C-S-H) were analyzed by molecular dynamics. Then, the mechanical and deformation properties under different stress states were simulated, and the characteristic parameters of the stress-strain relationships were compared and analyzed. The results show that the bulk modulus, shear modulus, and Young's modulus of C-S-H decrease with the increase of gel pore size. Gel pores will reduce the mechanical and deformation properties of C-S-H, but the effect varies under different stress states. Gel pores have the largest impact on tensile strength, and have almost the same effect on the compressive and shear strength. In terms of the gel pore effect on deformation properties, it is the largest under tension, followed by shearing, and finally compression. [ABSTRACT FROM AUTHOR]

In this article, an improved model‐free adaptive control method based on the controller dynamic linearization technique combined with the locally weighted regression‐based lazy learning method (cMFAC‐LL) is presented, and it is applied to solve the path‐tracking control problem for an NAO robot. In the proposed cMFAC‐LL method, two dynamic linearization techniques are first applied on the controlled plant, and then the cMFAC‐LL controller is further designed with the time‐varying parameters estimated using a novel locally weighted regression‐based lazy learning (LWR‐LL) technique. The greatest advantage of the cMFAC‐LL method is that it is a pure data‐driven control method, and the designed controller makes full use of both the online and offline measurement data of the plant. Moreover, the introduction of the local learning (LL) method gives the cMFAC‐LL method strong data learning ability and satisfactory control performance. The stability of cMFAC‐LL is proven via rigorous mathematical analysis. Furthermore, cMFAC‐LL is applied to the path‐tracking control of the NAO robot under different walking environments, by which the applicability of cMFAC‐LL is further verified. [ABSTRACT FROM AUTHOR]