{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"outputs": [],
"source": [
"import pandas as pd"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [],
"source": [
"import numpy as np\n",
"import time"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"$E = E_{燃烧} + E_{脱硫} + E_{电}$ \\\n",
"$E_{燃烧} = \\sum_i (AD_i\\times EF_i)$ \\\n",
"$AD_i = FC_i \\times NCV_i \\times 10^{-6}$ 消耗量*低位热值\n",
"$EF_i = CC_i \\times OF_i \\times \\frac{44}{12}$ 单位热值含碳量 * 碳氧化率"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [],
"source": [
"def cal_ad(fc, ncv):\n",
" return fc * ncv * 1e-6"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [],
"source": [
"def cal_ef(cc, of):\n",
" return cc * of * 44 / 12"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [],
"source": [
"cc_map = {\n",
" \"无烟煤\": 27.49,\n",
" \"烟煤\": 26.18,\n",
" \"褐煤\": 27.97,\n",
" \"贫煤\": 26.0,\n",
"}"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [],
"source": [
"data = pd.read_excel('./total_data.xlsx')"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 7,
"outputs": [],
"source": [
"data['入炉煤低位热值(kJ/kg)'] = data['入炉煤低位热值(kJ/kg)'].apply(lambda x: x*1000 if x < 100 else x*1)\n",
"data['燃煤灰份Aar(%)'] = data['燃煤灰份Aar(%)'].apply(lambda x: x / 1000 if x > 10000 else x*1)\n",
"data['燃煤挥发份Var(%)'] = data['燃煤挥发份Var(%)'].apply(lambda x: x / 1000 if x > 10000 else x*1)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [],
"source": [
"def cal_ctype(ncv, vdaf, ash):\n",
" if pd.isna(ncv) or pd.isna(vdaf) or pd.isna(ash):\n",
" return pd.NA\n",
" else:\n",
" if vdaf / (1 - ash/100 - 0.07196) < 10:\n",
" return '无烟煤'\n",
" elif vdaf / (1 - ash/100 - 0.06825) < 20:\n",
" return '贫煤'\n",
" elif vdaf / (1 - ash/100 - 0.08679) < 37 and ncv > 16730:\n",
" return '烟煤'\n",
" elif ncv < 16730:\n",
" return '褐煤'\n",
" else:\n",
" return '烟煤'"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 9,
"outputs": [],
"source": [
"data['煤种'] = data.apply(lambda x: cal_ctype(x['入炉煤低位热值(kJ/kg)'], x['燃煤挥发份Var(%)'], x['燃煤灰份Aar(%)']), axis=1)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 10,
"outputs": [],
"source": [
"data = data[~data['煤种'].isna()].copy()"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 11,
"outputs": [],
"source": [
"data = data[data['入炉煤低位热值(kJ/kg)']> 8000].copy()"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 12,
"outputs": [],
"source": [
"data = data[~((data['发电用标煤量（t）'].isna())|(data['发电用标煤量（t）']==0))].copy()\n",
"data = data[~((data['发电用标煤量（t）'].isna())|(data['发电用标煤量（t）']==0))].copy()"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 13,
"outputs": [
{
"data": {
"text/plain": "['出力系数(%).1', '出力系数(%).2', '化学系统耗电率 (%).1', '额再热蒸汽温度 (℃).1']"
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drop_cols = [x for x in data.columns if '.1' in x or '.2' in x]\n",
"drop_cols"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 14,
"outputs": [],
"source": [
"data.drop(columns=drop_cols, inplace=True)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 15,
"outputs": [],
"source": [
"data['工业供热量'].fillna(0, inplace=True)\n",
"data['采暖供热量'].fillna(0, inplace=True)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 16,
"outputs": [
{
"data": {
"text/plain": "array([ 56, 96, 122, ..., 16724, 16725, 16837], dtype=int64)"
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drop_index = data[(data['发电量(万kWh)']<10000)|(data['总供热量']<=10000)|(data['供热用标煤量（t）']==0)].index.values\n",
"drop_index"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 17,
"outputs": [],
"source": [
"data.drop(index=drop_index, inplace=True)\n",
"data.reset_index(inplace=True, drop=True)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 18,
"outputs": [],
"source": [
"data['发电碳排放因子(kg/kWh)'] = data.apply(lambda x: (cal_ad(x['发电用标煤量（t）'], x['入炉煤低位热值(kJ/kg)']) * cal_ef(cc_map.get(x['煤种']), 0.98))/x['发电量(万kWh)']/10, axis=1)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 19,
"outputs": [],
"source": [
"data['供热碳排放因子(kg/MJ)'] = data.apply(lambda x:(cal_ad(x['供热用标煤量（t）'], x['入炉煤低位热值(kJ/kg)']) * cal_ef(cc_map.get(x['煤种']), 0.98))/x['总供热量'] if x['总供热量'] != 0 else 0, axis=1)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 20,
"outputs": [
{
"data": {
"text/plain": " 总供热量 供热碳排放因子(kg/MJ)\n0 8.294556e+05 0.076843\n1 8.789928e+04 0.077676\n2 8.481764e+05 0.074823\n3 4.473469e+05 0.081628\n4 7.157164e+05 0.081103\n... ... ...\n7252 1.532303e+06 0.078776\n7253 2.147545e+06 0.076622\n7254 2.131207e+06 0.074772\n7255 3.039811e+06 0.091482\n7256 3.039813e+06 0.091483\n\n[7257 rows x 2 columns]",
"text/html": "\n\n
\n \n \n | \n 总供热量 | \n 供热碳排放因子(kg/MJ) | \n
\n \n \n \n | 0 | \n 8.294556e+05 | \n 0.076843 | \n
\n \n | 1 | \n 8.789928e+04 | \n 0.077676 | \n
\n \n | 2 | \n 8.481764e+05 | \n 0.074823 | \n
\n \n | 3 | \n 4.473469e+05 | \n 0.081628 | \n
\n \n | 4 | \n 7.157164e+05 | \n 0.081103 | \n
\n \n | ... | \n ... | \n ... | \n
\n \n | 7252 | \n 1.532303e+06 | \n 0.078776 | \n
\n \n | 7253 | \n 2.147545e+06 | \n 0.076622 | \n
\n \n | 7254 | \n 2.131207e+06 | \n 0.074772 | \n
\n \n | 7255 | \n 3.039811e+06 | \n 0.091482 | \n
\n \n | 7256 | \n 3.039813e+06 | \n 0.091483 | \n
\n \n
\n
7257 rows × 2 columns
\n
\n\n
\n \n \n | \n plant | \n longitude | \n latitude | \n altitude | \n
\n \n \n \n | 0 | \n 万方发电厂（焦作爱依斯万方电力有限公司) | \n 113.381649 | \n 35.255622 | \n 88.0 | \n
\n \n | 1 | \n 三河发电有限责任公司 | \n 116.860260 | \n 39.953617 | \n 27.0 | \n
\n \n | 2 | \n 上海上电漕泾发电有限公司 | \n 121.407593 | \n 30.765242 | \n 4.0 | \n
\n \n | 3 | \n 上海吴泾发电有限责任公司 | \n 121.471140 | \n 31.065113 | \n 3.0 | \n
\n \n | 4 | \n 上海吴泾第二发电有限责任公司 | \n 121.471340 | \n 31.062532 | \n 4.0 | \n
\n \n
\n
\n\n
\n \n \n | \n | \n 铭牌容量 (MW) | \n len | \n
\n \n | 电厂名称 | \n 机组编号 | \n | \n | \n
\n \n \n \n
\n
\n\n
\n \n \n | \n 电厂名称 | \n 机组编号 | \n 铭牌容量 (MW) | \n 投产时间 | \n 机组类型 | \n 参数分类 | \n 所处地区 | \n 冷凝器型式 | \n 时间 | \n 发电量(万kWh) | \n ... | \n 引风机最大风压(Pa) | \n 引风机最大流量(m3/S） | \n 引风机电机电压(V) | \n 引风机电机功率（KW） | \n 煤种 | \n 发电碳排放因子(kg/kWh) | \n 供热碳排放因子(kg/MJ) | \n longitude | \n latitude | \n altitude | \n
\n \n \n \n
\n
0 rows × 134 columns
\n