Effect of clay and guishe-ash as substrate on Lycopersicon esculentum germination and production.

The effect of biomass ash and clay on tomato plants (Lycopersicon esculentum) in greenhouse conditions from germination to production was studied. Biomass ash is a waste obtained from thermal treatment of guishe (a by-product of natural fiber), and clay is collected from local soils. Several trials were performed to assess the influence of the addition of clay and guishe-ash on seeds germination, seedling growth, and production yield. The decrease in the values of these variables was considered an indicator of toxicity. The obtained results showed that based clay/ash materials positively affect germination (average ∼90% and six materials allow obtaining 90%) and seedlings growth (an increase of ∼20% in height and more than 50% in fresh air corpuscular weight). However, applying these materials on the production stage induces minor positive effects on fruit diameter, locule number, pericarp thickness, and the number of seeds per fruit. Also, adverse effects (first harvest yield, number of fruits, fresh mass of ripe fruits, lycopene content) were observed. To valorize biomass ash, its combination with other materials such as clay could be an alternative to improve tomato production.

The concern to attend the growing demand for food has promoted the use of different kinds of materials as enhancers of plant growth and crop productivity. Among the materials that have been applied in crops are the wastes of biomass thermochemical processes, such as biochar and ashes. This work highlights the importance of evaluating the effect of applying a residue (guishe-ash) to a crop before promoting its use.

A neural observer with time-varying learning rate: analysis and applications.

In this paper, a reduced order neural observer (RONO) with a time-varying learning rate is proposed. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. A time-varying learning rate is designed in order to improve the learning of the neuronal network in presence of disturbances and parameter variations. This work includes the stability proof of the time-varying learning. The applicability of the developed observer is illustrated via simulations for a nonlinear anaerobic digestion process.

A new neural observer for an anaerobic bioreactor.

In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed. The main objective is to estimate variables of methanogenesis: biomass, substrate and inorganic carbon in a completely stirred tank reactor (CSTR). The recurrent high order neural network (RHONN) structure is based on the hyperbolic tangent as activation function. The learning algorithm is based on an extended Kalman filter (EKF). The applicability of the proposed scheme is illustrated via simulation. A validation using real data from a lab scale process is included. Thus, this observer can be successfully implemented for control purposes.