The Ganges Delta is a key area where elemental contamination of groundwater constitutes a human catastrophe. The delta plain geomorphology comprises a large number of abandoned meander bends or oxbow lakes (Donselaar et al., 2017; Ghosh et al., 2021) characterized by an anoxic environment in the lower part of the lake water column (hypolimnion). Here we present the critical role of these abandoned-river channels forming oxbow lakes. The geomorphological the juxtaposition of (a) abandoned channels (or: oxbow lakes) where the cocktail of organic matter and sediment leads to the release of various elements, (b) the topographically higher point bars where the released elements accumulate in the aquifer and provide a blueprint to explain the origin and localization of elemental toxicity. Dissolved organic matter (DOM) is implicated in the mobilization of elements via microbial metabolic processes. Organic matter (OM) is preserved in this environment and provides a perfect environment for microbial oxidation and mobilization of Fe-oxides. Additional deposition of human-introduced sewage wastes adds to a rich source of nutrients to the indigenous microbial communities. A multidisciplinary approach was effective in understanding the geomorphology of river meanders, forming abandoned channels, which act as a growth bed for biomass. While acting as an incubator for primary production (lake vegetation dynamics), and subsequent organic debris accumulation (anoxic, hypolimnion water column), where selective preferential preservation of organic carbon compound (anoxic sediment base) occur. We have described how organic compound infiltration, deposition and abundance depends on their hydrophobicity, molecular weights and bioavailability and further, due to diagenetic alteration (microbial metabolic oxidation). Different classes of surface derived organic carbon from vegetation with anthropogenic inputs, can have different effects on the mineral weathering and in controlling the downstream cationic fluxes such as Fe, Mn, As, F etc. and contamination of aquifers in various river plains across the world. @en

Several classes of multi-model adaptive control schemes have been proposed in literature: instead of one single parameter-varying controller, in this adaptive methodology multiple fixed-parameter controllers for different operating regimes (i.e. different models) are utilised. Despite advances in multi-model adaptive control theory, the question of how the synthesis of the pairs model/controller will affect transient and steady-state performance is not completely addressed. In particular, it is not clear to which extent placing the pairs model/controller in a structurally optimal way will result in a relevant improvement of the properties of the switching algorithm. In this study the authors focus on a multi-model unfalsified adaptive supervisory switching control scheme, and they show how the minimisation of a suitable structural criterion can lead to improved performance of the adaptive scheme. The peculiarity of the resulting structural optimality criterion is that the optimisation is carried out so as to optimise the entire behaviour of the adaptive algorithm, i.e. both the learning transient and the steady-state response. This is in contrast to alternative multi-model adaptive control schemes, where special structural optimisation considers only the steady-state ideal response and neglects learning transients. A comparison with respect to model distributions achieved via two structural optimisation criteria is made via a benchmark example.@en

Several multiple model adaptive control architectures have been proposed in the literature. Despite many advances in theory, the crucial question of how to synthesize the pairs model/controller in a structurally optimal way is to a large extent not addressed. In particular, it is not clear how to place the pairs model/controller is such a way that the properties of the switching algorithm (e.g., number of switches, learning transient, final performance) are optimal with respect to some criteria. In this work, we focus on the so-called multi-model unfalsified adaptive supervisory switching control (MUASSC) scheme; we define a suitable structural optimality criterion and develop algorithms for synthesizing the pairs model/controller in such a way that they are optimal with respect to the structural optimality criterion we defined. The peculiarity of the proposed optimality criterion and algorithms is that the optimization is carried out so as to optimize the entire behavior of the adaptive algorithm, i.e., both the learning transient and the steady-state response. A comparison is made with respect to the model distribution of the robust multiple model adaptive control (RMMAC), where the optimization considers only the steady-state ideal response and neglects any learning transient. @en