This work deals with the problem of choosing a controller for the production of biodiesel from the transesterification process through temperature control of the chemical reactor, from the point of view of automatic control, by considering such aspects as the performance metrics based on the error and the energy used by the controller, as well as the evaluation of the control system before disturbances. In addition, an improvement method is proposed via a neuro-fuzzy controller tuned with a metaheuristic algorithm to increase the efficiency of the chemical reaction in the reactor. A clear improvement is shown in the minimization of the integral of time multiplied squared error criterion (ITAE) performance index with respect to the proposed method (8.1657 $\times {10}^4$) in relation to the PID controller (7.8770 $\times {10}^7$). Moreover, the integral of the total control variation (TVU) performance index is also shown to evaluate the power used by the neuro-fuzzy controller (25.7697), while the PID controller obtains an index of (32.0287); this metric is especially relevant because it is related to the functional requirements of the system since it quantifies the variations of the control signal.

Recent developments in semi-active control technologies enhance the possibility of an effective response reduction during a wide range of dynamic loading conditions. Most semi-active control schemes employ magnetorheological dampers (MRDs) as actuators. These devices exhibit nonlinear and hysterical behavior that complicates reactive force estimation to compensate for disturbances. In this paper, we present a novel robust schema to estimate MRD forces using a complex value convolutional neural network (CV-CNN) to overcome these problems. CV-CNN utilizes random complex value convolutional filters as parameters to reduce the measured noise by combining the training stage and the max-by-magnitude operation. Furthermore, CV-CNN is a hysteresis-model-free strategy that overcomes the parameterization in nonlinear systems. The proposed CV-CNN only requires displacement and voltage measurements for force estimation. Different metrics are used to compare results between the CV-CNN, genetic algorithm (GA), particle swarm optimization (PSO), and shallow neural network (SNN). Experimental results show the potential of the proposed CV- CNN for practical applications due to its simplicity and robustness. The CV-CNN computational time is less than that of GA and PSO. In the training stage, the CV-CNN uses 0.7% of GA's time and 1.4% of PSO. Although SNN uses 5.5% of the time consumed by the CV-CNN, the latter performs the force estimation for MRD better; its mean square error is 78.3% lower than the GA's and PSO's, and 71.4% lower than SNN's.

Heat exchangers are widely used process equipment in industrial sectors, making the study of their temperature dynamics particularly appealing due to the nonlinearities involved. Model-free approaches enable the use of input and output data to generate specific and accurate estimations for each proposed system. In this work, a model-free identification strategy is proposed using a convolutional neural network to estimate the system’s behavior. Notably, the model does not rely on direct temperature measurements; instead, temperature is inferred from other system signals such as reference, flow, and control inputs. This data-driven approach offers greater specificity and adaptability, often outperforming manufacturer-provided coefficients whose performance may vary from design expectations. The results yielded an $R^2$ index of 0.9951 under nominal conditions and 0.9936 when the system was subjected to disturbances.

This paper demonstrates that biodiesel production processes can be optimized through implementing a controller based on fuzzy logic and neural networks. The system dynamics are identified utilizing convolutional neural networks, enabling tests of the reactor temperature response under different control law proposals. In addition, a sensorless technique using a convolutional neural network to replace the sensor/transmitter signal in case of failure is implemented. Two optimization functions are proposed utilizing a metaheuristic algorithm based on differential evolution, where the aim is to minimize the use of cooling for the control of the reactor temperature. Finally, the control system proposals are compared, and the results show that a neuro-fuzzy controller without optimization restrictions generated unviable ITAE ($1.9597\times {10}^7$) and TVU (22.3993) performance metrics, while the restriction proposed in this work managed to minimize these metrics, improving both the ITAE ($3.3928\times {10}^6$) and TVU (17.9132). These results show that combining the sensorless technique and our optimization method for the cooling stage enables energy saving in the temperature control processes required for biodiesel production.

Determining the position values of the effectors in a robot to enable its end effector to perform a specific task is a recurrent challenge in robotics. Diverse methodologies have been explored to address this problem, each with distinct advantages and limitations. This work proposes a metaheuristic-based approach to solve a sequence of optimization problems associated with the discretized trajectory of the end effector. Additionally, a method to identify a functional model that describes the effector trajectories is introduced using the same optimization technique. The key contribution lies in algorithmic adjustments that enhance the metaheuristic solutions by leveraging the behavior of the robot and the influence of the tracking task on the search space. Specifically, two operations are modified in the initialization process of the candidate solution. The proposed biased initialization with variable weights improves positional accuracy (72.5%) in relation to methods without dynamic updates. Additionally, the standard deviation was reduced by (89%). For industrial implementations, modern controllers can directly encode effector positions via parametric functions. The results of this proposal formulate optimization problems whose solutions yield the parameters of a time-dependent mathematical model describing the movement of the effector.