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The number of wires needed for effecting control increases with the complexity of the automation. In the last years, this has limited the development of process automation in the industry. Wireless networks offer an evolution to these restrictions, bringing facilities in reconfiguration and portability to mobile terminals. Meanwhile, some paradigms as, for example, security, reliability and latency, make it difficult for new technologies to be inserted into the industrial area. One of the major difficulties in wirelessly networked control systems is that the delay induced by the network in the control loop may cause degradation of the control performance and can destabilize the plant. This research offers a general view of the WirelessHART protocol for networked control systems, including the necessary mechanisms for an effective control loop through a host application. It also shows that is possible to perform control over WirelessHART networks with the control module placed in a host application using an acceptable working sample period for a process plant in a networked control system.

Background Microalgal cultures are evolving into a promising ecofriendly technology for a host of applications. To be sustainable, culture conditions need to be optimized and then controlled. One way to develop robust controllers for a cultivation system is by using mathematical growth models to simulate microalga‐based production. In this scenario, engineering design tasks begin by selecting the critical variables of these models. Results A new methodology for determining the significance ranking of a model's input factors under steady‐state operation (parameters (e.g. biological, geometrical) and/or process variables) was designed. The sensitivity of biomass response to its inputs was investigated in four different photobioreactor growth models within a nominal operational region. The methodology ranks models’ input factors based on the one‐at‐a‐time Morris method of elementary effects and variance‐based Sobol's method. Such information provided by the presented procedure is valuable as it reveals which input parameters explain most of the variance in model predictions. Conclusion The methodology allowed the identification of controlled variables and biological parameters to be targeted for enhanced calibration. Furthermore, the presented methodology showed that in continuous reactors the dilution rate is a critical variable of the process. Therefore, it should be controlled. Additionally, most surprisingly, it is observed that controlling the light intensity within the optimum point of operation is not necessarily a crucial task. However, although its manipulation is still important, the accurate calibration of the parameters of the model may represent a greater influence on the biomass response. © 2023 Society of Chemical Industry (SCI).