This paper deals with a neural network based GPC structure for a bioprocess control. Comparing to IMC structure, this method offers two advantages: the neural inverting operation of the process model is eliminated and there are various possibilities to adjust the control law properties. The GPC method is applied to a biomass production process and to an enzymatic production process (lipase prod...

In this paper a new design method of a self-tuning PID controller is proposed. The PID controller is designed based on generalized predictive control (GPC) including a future reference trajectory. The PID controller given in this paper is based on GPC and, is I-PD type controller. It is shown that the design parameter adjusting the future reference trajectory changes only the integral time of t...

In cereals, senescence is a highly regulated and organized process in which the nutrients located in the leaves are translocated to the developing seeds. During this process multiple signaling cascades are activated either up-regulating or down-regulating senescence related genes. The majority of the nitrogen to be remobilized is located in the leaves in plastidial proteins, especially Rubisco....

We introduce an operator splitting based stochastic Galerkin method for the one-dimensional compressible Euler equations with random inputs. The method uses a generalized polynomial chaos approximation in the stochastic Galerkin framework (referred to as the gPC-SG method). It is well-known that such approximations for nonlinear system of hyperbolic conservation laws do not necessarily yield gl...

A series of binuclear nickel/palladium catalysts C4-C7 with conjugated α-diimine ligands were designed, prepared and fully characterized. The binuclear nickel complexes C6 and C7 were activated by modified methylalumoxanes (MMAO) to generate highly active ethylene polymerization catalysts with activities up to 1050 kg [mol (Ni) h](-1). The activity of C7 is twice that of the mononuclear analogu...

In this paper we present a Multi-Element generalized Polynomial Chaos (MEgPC) method to deal with stochastic inputs with arbitrary probability measures. Based on the decomposition of the random space of the stochastic inputs, we construct numerically a set of orthogonal polynomials with respect to a conditional probability density function (PDF) in each element and subsequently implement genera...