This work describes the evaluation of the solid-state fermentation (SSF) bioprocess utilizing brewery spent grain (BSG) and apple pomace (AP) as carbon sources and matrices for microorganism growth to produce xylanase, pectinase, and cellulase. The process was assessed at a larger scale by designing a packed column-type bioreactor equipped with sensors for monitoring critical parameters such as CO₂ concentration, humidity, and temperature. Then, process simulation was used to evaluate the techno-economic feasibility of the bioprocess at an industrial scale. The analysis centered on evaluating which formulation, primarily containing xylanase (scenario 1), pectinase (scenario 2), or cellulase (scenario 3), yielded the most promising results for advancing to the commercial stage. Additionally, a sensitivity analysis was conducted to explore the influence of variations in raw material costs and enzyme prices. The obtained results at a higher scale were within the expected results obtained under optimum conditions. Scenario 1 exhibited strong economic viability with further optimization potential (base case: 5000 kg/batch with an ROI of 37.59%, payback time of 2.66 years, IRR of 26.8%, and net present value of USD 7,325,537). The sensitivity analysis revealed that changes in enzyme prices, particularly xylanase, could significantly influence the process’s profitability. This study also demonstrated the potential for cost optimization by selecting a more cost-effective inoculum media and optimizing water usage to enhance process efficiency and sustainability.

A multi-agent system (MAS) is suitable for addressing tasks in a variety of domains without any programmed behaviors, which makes it ideal for the problems associated with the mobile robots. Reinforcement learning (RL) is a successful approach used in the MASs to acquire new behaviors; most of these select exact Q-values in small discrete state space and action space. This article presents a joint Q-function linearly fuzzified for a MAS’ continuous state space, which overcomes the dimensionality problem. Also, this article gives a proof for the convergence and existence of the solution proposed by the algorithm presented. This article also discusses the numerical simulations and experimental results that were carried out to validate the proposed algorithm.

This article presents a methodology to recycle and upgrade a 4-DOF educational robot manipulator with a gripper. The robot is upgraded by providing it an artificial vision that allows obtaining the position and shape of objects collected by it. A low-cost and open-source hardware solution is also proposed to achieve motion control of the robot through a decentralized control scheme. The robot joints are actuated through five direct current motors coupled to optical encoders. Each encoder signal is fed to a proportional integral derivative controller with anti-windup that employs the motor velocity provided by a state observer. The motion controller works with only two open-architecture Arduino Mega boards, which carry out data acquisition of the optical encoder signals. MATLAB-Simulink is used to implement the controller as well as a friendly graphical interface, which allows the user to interact with the manipulator. The communication between the Arduino boards and MATLAB-Simulink is performed in real-time utilizing the Arduino IO Toolbox. Through the proposed controller, the robot follows a trajectory to collect a desired object, avoiding its collision with other objects. This fact is verified through a set of experiments presented in the paper.

An induction motor is an electric machine widely used in various industrial and commercial applications due to its efficiency and simple design. In this regard, a methodology based on the electric phasor analysis of line currents and the variations in the phase angles among these line currents is proposed. The values in degrees of the angles between every pair of line currents were introduced to a fuzzy logic algorithm based on the Mamdani model, developed using the Matlab toolbox for detection and isolation of the inter-turn short-circuit faults on the windings of an induction motor. To carry out the analysis, the induction motor was modified in its stator windings to artificially induce short-circuit faults of different magnitudes. The current signals are acquired in real time using a digital platform developed in the Delphi 7 high-level language communicating with a float point unit Digital Signal Processor (DSP) TMS320F28335 by Texas Instruments. The proposed method not only detects the short circuit faults but also isolates the faulty winding.

The auto-tuning proportional-integral-derivative (PID) controllers based on genetic algorithms have many limitations in their implementation. For example, some chromosomes of the tuned genetic algorithms may cause closed-loop system instability, and some chromosomes tuned for an operating condition may cause instability for other operating conditions. This article provides the possible solutions for these problems. An implementation technique of a genetic algorithm is proposed based on these solutions. Numerical simulations are given to evaluate the proposed method applied to a system with variable parameters. Moreover, this technique is also used for controlling a brushless servo motor.