Pharmaceutical industries widely use Escherichia coli cell strain to synthesize various target products. The main goal is to reach the highest possible product yield. However, the formation of by-products is inevitable throughout the cell growth stage. Metabolic compounds such as acetates cause inhibition, particularly in later bioprocess stages. Therefore, the acetate accumulation model is necessary for planning bioprocesses to maximize cell biomass growth. The decision tree method was in possession to replicate the approach. Specific biomass growth at induction, broth weight, oxygen uptake rate, and consumed substrate weight were the inputs of model training. Broth and consumed substrate weight had additional aging-related information incorporated as separate inputs to introduce the cumulative regularization.

This study proposes a novel method for the positioning and spatial orientation control of three inextensible segments of trunk-type robots. The suggested algorithm imposes a soft constraint assumption for the end-effector’s endpoint and a mandatory constraint on its direction. Simultaneously, the algorithm by-design enforces nonholonomic features on the robot segments in the form of arcs. An approximate robot spine curve is the key to the final robot state configuration based on the given conditions. The numeric simulation showed acceptable (less than 1 s) performance for single-core processing tasks. The parametric method finds the best proximate robot state solution and represents the gray box model in addition to existing learning or black-box inverse dynamics approaches. This study also shows that a multiple inverse kinematics answer constructs a single inverse dynamics solution that defines the robot actuators’ motion profiles, synchronized in time. Finally, this text presents rotational expressions and their outlines for controlling the manipulator’s tendons.

Accurate estimations of the concentrations of soluble compounds are crucial for optimizing bioprocesses involving Escherichia coli (E. coli). This study proposes a hybrid model structure that leverages off-gas analysis data and physiological parameters, including the average biomass age and specific growth rate, to estimate soluble compounds such as acetate and glutamate in fed-batch cultivations We used a hybrid recurrent neural network to establish the relationships between these parameters. To enhance the precision of the estimates, the model incorporates ensemble averaging and information gain. Ensemble averaging combines varying model inputs, leading to more robust representations of the underlying dynamics in E. coli bioprocesses. Our hybrid model estimates acetates with 1% and 8% system precision using data from the first site and the second site at GSK plc, respectively. Using the data from the second site, the precision of the approach for other solutes was as fallows: isoleucine −8%, lactate and glutamate −9%, and a 13% error for glutamine., These results, demonstrate its practical potential.