Lluís Godo-Pla

The main aim of this paper is defining a Logic of Formal Inconsistency over the degree-preserving companion of Nelson logic with a consistency operator. In this sense, we present a quasivariety of Nelson lattices enriched with a suitable consistency operator and axiomatise the corresponding logic. As main results we present necessary and sufficient conditions to prove a categorial equivalence for the category for Nelson lattices with a consistency operator.

Drinking water production is subject to multiple water quality requirements such as minimizing disinfection byproducts (DBPs) formation, which are highly related to natural organic matter (NOM) content. For water treatment, coagulation is a key process for removing water pollutants and, as such, is widely implemented in drinking water treatment plants (DWTPs) facilities worldwide. In this context, artificial intelligence (AI) tools can be used to aid decision making. This study presents an environmental decision support system (EDSS) for coagulation in a Mediterranean DWTP. The EDSS is structured hierarchically into the following three levels: data acquisition, control, and supervision. The EDSS relies on influent water characterization, suggesting an optimal pH and coagulant dose. The model designed for the control level is based on response surface methodology (RSM), targeted to optimize removal for the response variables (turbidity, total organic carbon (TOC), and UV₂₅₄). Results from the RSM model provided removal percentages for turbidity (64.6%), TOC (21.9%), and UV₂₅₄ (30%), which represented an increase of 4%, 33%, and 28% as compared with the DWTP water sample. Regarding the entire EDSS, 62%, 21%, and 25% of turbidity, TOC, and UV₂₅₄ removal were fixed as the optimization criteria. Supervision rules (SRs) were included at the top of the architecture to intensify process performance under specific circumstances.

Drinking Water Treatment Plants (DWTPs) face changes in raw water quality and treatment needs to be adjusted to produce the best water quality at the minimum environmental cost. An environmental decision support system (EDSS) was developed for aiding DWTP operators in choosing the adequate permanganate dosing rate in the pre-oxidation step. To this end, multiple linear regression (MLR) and multi-layer perceptron (MLP) models are compared for choosing the best predictive model. Besides, a case-based reasoning (CBR) model was approached to provide the user with a distribution of solutions given similar operating conditions in the past. The predictive model consisted of an MLP and has been validated against historical data with sufficient good accuracy for the utility needs (R² = 0.76 and RSE = 0.13 mg·L⁻¹). The integration of the predictive and the CBR models in an EDSS gives the user an augmented decision-making capacity of the process and has great potential for both assisting experienced users and for training new personnel in deciding the operational set-point of the process.