In earlier issues of this bulletin, Haverly and Hart discussed solving some simple pooling problems using LP recursion [1]-[3]. We thought it would be interesting to attempt to solve these problems using some nonlinear programming (NLP) codes. This note briefly discusses the codes and algorithms used, presents the results, and discusses some properties of the models.

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This paper presents the development of a climate dynamics model for a greenhouse located on the Bogota plateau. A black-box model was estimated from experimental data, considering a novel system structure derived from a first principles model and experimental tests. It considered two control volumes, one given by the air over the crop and a second one formed by the air trapped by crop foliage. The model was selected from a set of linear, discrete-time, state-variable systems using subspace identification methods. The estimated system was able to predict climate dynamics for both control volumes, having errors below 8%. Such performance was comparable to previous work reported in literature while the obtained model was a low-complexity linear system.