diff --git a/cymetric/metrics.py b/cymetric/metrics.py
index 605ddb74..d76e68d9 100644
--- a/cymetric/metrics.py
+++ b/cymetric/metrics.py
@@ -304,12 +304,15 @@ def fco_u_mined(series):
     mass235 = {}
     m = mass[mass['Commodity'] == 'LWR Fuel']
     for (obj, _, nuc), value in m.iterrows():
-        prods[obj] = prods.get(obj, 0.0) + value['Mass']
+        if 922320000 <= nuc <= 922390000:
+            prods[obj] = prods.get(obj, 0.0) + value['Mass']
         if nuc==922350000:
             mass235[obj] = value['Mass']
+    x_feed = 0.0072
+    x_tails = 0.0025
     for obj, m235 in mass235.items():
         x_prod = m235 / prods[obj]
-        feed = enr.feed(0.0072, x_prod, 0.0025, product=prods[obj]) / 1000
+        feed = enr.feed(x_feed, x_prod, x_tails, product=prods[obj]) / 1000
         u.append(feed)
     m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
     m = m.reset_index()
@@ -323,6 +326,50 @@ def fco_u_mined(series):
 del _udeps, _uschema
 
 
+# SWU Required [million SWU]
+_swudeps = [
+    ('Materials', ('ResourceId', 'ObjId', 'TimeCreated', 'NucId'), 'Mass'),
+    ('Transactions', ('ResourceId',), 'Commodity')
+    ]
+
+_swuschema = [('Year', ts.INT), ('SWU', ts.DOUBLE)]
+
+@metric(name='FcoSwu', depends=_swudeps, schema=_swuschema)
+def fco_swu(series):
+    """FcoSwu metric returns the separative work units required for each 
+    year in a 200-yr simulation. This is written for FCO databases that 
+    use the Bright-lite (i.e., the U235 and U238 are given separately 
+    in the FCO simulations).
+    """
+    tools.raise_no_pyne('SWU Required could not be computed', HAVE_PYNE)
+    mass = pd.merge(series[0].reset_index(), series[1].reset_index(),
+            on=['ResourceId'], how='inner').set_index(['ObjId', 'TimeCreated', 'NucId'])
+    swu = []
+    prods = {}
+    mass235 = {}
+    m = mass[mass['Commodity'] == 'LWR Fuel']
+    for (obj, _, nuc), value in m.iterrows():
+        if 922320000 <= nuc <= 922390000:
+            prods[obj] = prods.get(obj, 0.0) + value['Mass']
+        if nuc == 922350000:
+            mass235[obj] = value['Mass']
+    x_feed = 0.0072
+    x_tails = 0.0025
+    for obj, m235 in mass235.items():
+        x_prod = m235 / prods[obj]
+        swu0 = enr.swu(x_feed, x_prod, x_tails, product=prods[obj]) / 1e6
+        swu.append(swu0)
+    m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
+    m = m.reset_index()
+    # sum by years (12 time steps)
+    swu = pd.DataFrame(data={'Year': m.TimeCreated.apply(lambda x: x//12),
+                             'SWU': swu}, columns=['Year', 'SWU'])
+    swu = swu.groupby('Year').sum()
+    rtn = swu.reset_index()
+    return rtn
+
+del _swudeps, _swuschema
+
 # Electricity Generated [GWe-y]
 _egdeps = [('TimeSeriesPower', ('Time',), 'Value'),]
 
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 60fa33bd..a131a205 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -241,6 +241,39 @@ def test_fco_u_mined():
     assert_frame_equal(exp, obs)
 
 
+def test_fco_swu():
+    if not HAVE_PYNE:
+        raise SkipTest
+    exp = pd.DataFrame(np.array([(0, 0.002407), (1, 0.001473)], 
+        dtype=ensure_dt_bytes([('Year', '<i8'), ('SWU', '<f8')]))
+        )
+    mats = pd.DataFrame(np.array([
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922350000, 8.328354),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922380000, 325.004979),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922350000, 11.104472),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922380000, 322.228861),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922350000, 11.104472),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922380000, 322.228861),
+        ], dtype=ensure_dt_bytes([
+                ('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'),
+                ('ObjId', '<i8'), ('TimeCreated', '<i8'), ('NucId', '<i8'), 
+                ('Mass', '<f8')]))
+        )
+    trans = pd.DataFrame(np.array([
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 7, 'LWR Fuel'),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 8, 'LWR Fuel'),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 9, 'LWR Fuel'),
+        ], dtype=ensure_dt_bytes([
+                ('SimId', 'O'), ('TransactionId', '<i8'), ('ResourceId', '<i8'), 
+                ('Commodity', 'O')]))
+        )
+    s1 = mats.set_index(['SimId', 'QualId', 'ResourceId', 'ObjId', 'TimeCreated', 'NucId'])['Mass']
+    s2 = trans.set_index(['SimId', 'TransactionId', 'ResourceId'])['Commodity']
+    series = [s1,s2]
+    obs = metrics.fco_swu.func(series)
+    np.allclose(exp, obs)
+
+
 def test_fco_fuel_loading():
     exp = pd.DataFrame(np.array([(0, 0.666666), (1, 0.333333)], 
         dtype=ensure_dt_bytes([('Year', '<i8'), ('FuelLoading', '<f8')]))