introduced a data-driven technique to estimate conditions at which a process should operate (i.e., temperature, pressure, and reactant amountssrecipe) in order to yield a final product with a desired set of quality characteristics. Their proposed technique utilizes empirical latent variable models that are fitted to historical process data from existing process grades. This paper extends the methodology to include estimation of the entire set of time-Varying profiles for the manipulated Variables for batch processes. The problem is formulated in an optimization framework to include both equality and inequality constraints in the objective function. Since often the solution is not unique, the locus of the multiple solutions (defined as the null space) is studied and approaches to selecting the best solution for the final variable settings and trajectories are discussed. Finally, a parallel approach based on a deriVatiVe-augmented model is suggested that avoids considering null spaces to select the final design. An industrial batch digester from the pulp and paper industry is considered throughout the paper to explain and illustrate the key concepts.

No abstract

Sparrow (J Appl Philos 24:62-77, 2007) argues that military robots capable of making their own decisions would be independent enough to allow us denial for their actions, yet too unlike us to be the targets of meaningful blame or praise-thereby fostering what Matthias (Ethics Inf Technol 6:175-183, 2004) has dubbed "the responsibility gap." We agree with Sparrow that someone must be held responsible for all actions taken in a military conflict. That said, we think Sparrow overlooks the possibility of what we term "blank check" responsibility: A person of sufficiently high standing could accept responsibility for the actions of autonomous robotic devices-even if that person could not be causally linked to those actions besides this prior agreement. The basic intuition behind our proposal is that humans can impute relations even when no other form of contact can be established. The missed alternative we want to highlight, then, would consist in an exchange: Social prestige in the occupation of a given office would come at the price of signing away part of one's freedoms to a contingent and unpredictable future guided by another (in this case, artificial) agency.

This paper investigates an approach to modeling and optimizing an industrial tablet manufacturing line for different API and excipient formulations. Multi-block partial lease square (PLS) models are built from historical data on a given class of drug products. The data blocks consisted of data on the mass fractions of API and 11 excipients used in the different formulations, the roller compaction process variables, the tablet press settings and the measured final product quality attributes (tablet weight, hardness, and disintegration time). More than 400 runs are used in the modeling. The multi-block PLS models are first used to show which process blocks and which variables in each of the process blocks are most influential on the product quality variables. An optimization is then performed in the latent variable space of the PLS model to find the optimal combination of settings to use for the critical to quality roller compaction and tablet press variables in order to achieve the desired final tablet properties for a specified drug formulation. This optimization can be used to set up the tableting line prior to running a new formulation or can be used in an on-line mode for making small corrections to the operation of the tablet presses in response to small variations in formulations, raw material properties, and roller compaction operation.