Perancangan Sistem Pengendali Komposisi Pada Kolom Demethanizer Dan Deethanizer Berbasis Artificial Neural Network (ANN)

Kolom distilasi seperti demethanizer dan deethanizer merupakan sistem multi-input multi-output (MIMO) yang memiliki karakteristik nonlinear. Dalam tugas akhir ini dirancang sistem Model Predictive Control (MPC) berbasis neural network. Input kontrol dibangkitkan berdasarkan output yang telah diprediksi dengan menggunakan model neural network. Model neural network dibuat berdasarkan hasil pengambilan data open loop dan dibagi menjadi dua model NN MISO. Model NN MISO 1 menunjukkan hasil MSE pelatihan sebesar 0,00008792 dengan hasil pengujian regresi (R) sebesar 0,9957. Model NN MISO 2 menunjukkan hasil MSE pelatihan sebesar 0,00001868 dengan hasil pengujian regresi (R) sebesar 0,9785. Perancangan pengendali NNMPC 1 untuk mengendalikan komposisi methane dihasilkan performa dengan settling time sebesar 36 detik, error steady state sebesar 0,001053%, dan maximum overshoot sebesar 3,921%. Pengendali NNMPC 2 untuk mengendalikan komposisi ethane dihasilkan performa dengan settling time sebesar 45 detik, error steady state sebesar 0,0007%, dan maximum overshoot sebesar 1,8911%. Pengujian disturbance juga dilakukan dengan hasil settling time sebesar 233 detik, error steady state sebesar 0,00021%, dan maximum overshoot sebesar 0,1381% untuk pengendali NNMPC 1. Pengendali NNMPC 2 menghasilkan performa dengan hasil settling time sebesar 220 detik, error steady state sebesar 0,00032%, dan maximum overshoot sebesar 0,0793%. ====================================================================================================== The distillation columns such as demethanizer dan deethanizer are multi-input multi-output (MIMO) systems which has nonlinear characteristics. In this final project, a neural network-based Model Predictive Control (MPC) system is designed. The control input is generated based on the predicted output using a neural network model. Neural network model is based on results of open loop data. The NN MISO 1 model shows MSE results of 0.00008792 with R value of 0.9957. The NN MISO 2 model shows MSE results of 0.00001868 with R value of 0.9785. NNMPC 1 is used to control the methane composition resulted in performance with 36 seconds settling time, 0.001053% ess, and 3.921% maximum overshoot. NNMPC 2 controller to control the ethane composition resulted in a performance with 45 seconds settling time, 0.0007% ess, and 1.8911% maximum overshoot. The disturbance test was also carried out with the results of 233 seconds settling time, 0.00021% ess, and 0.1381% maximum overshoot for the NNMPC 1 controller. NNMPC 2 controller produced a performance of 220 seconds settling time, 0.00032% ess, and 0.0793% maximum overshoot

The Egyptian Predynastic and State Formation

When the archaeology of Predynastic Egypt was last appraised in this journal, Savage (2001a, p. 101) expressed optimism that “a consensus appears to be developing that stresses the gradual development of complex society in Egypt.” The picture today is less clear, with new data and alternative theoretical frameworks challenging received wisdom over the pace, direction, and nature of complex social change. Rather than an inexorable march to the beat of the neo-evolutionary drum, primary state formation in Egypt can be seen as a more syncopated phenomenon, characterized by periods of political experimentation and shifting social boundaries. Notably, field projects in Sudan and the Egyptian Delta together with new dating techniques have set older narratives of development into broader frames of reference. In contrast to syntheses that have sought to measure abstract thresholds of complexity, this review of the period between c. 4500 BC and c. 3000 BC transcends analytical categories by adopting a practice-based examination of multiple dimensions of social inequality and by considering how the early state may have become a lived reality in Egypt around the end of the fourth millennium BC